Showing papers in "Mechanical Systems and Signal Processing in 2011"
TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
Abstract: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears. Rather than being a review of all the current literature on bearing diagnostics, its purpose is to explain the background for a very powerful procedure which is successful in the majority of cases. The latter contention is illustrated by the application to a number of very different case histories, from very low speed to very high speed machines. The specific characteristics of rolling element bearing signals are explained in great detail, in particular the fact that they are not periodic, but stochastic, a fact which allows them to be separated from deterministic signals such as from gears. They can be modelled as cyclostationary for some purposes, but are in fact not strictly cyclostationary (at least for localised defects) so the term pseudo-cyclostationary has been coined. An appendix on cyclostationarity is included. A number of techniques are described for the separation, of which the discrete/random separation (DRS) method is usually most efficient. This sometimes requires the effects of small speed fluctuations to be removed in advance, which can be achieved by order tracking, and so this topic is also amplified in an appendix. Signals from localised faults in bearings are impulsive, at least at the source, so techniques are described to identify the frequency bands in which this impulsivity is most marked, using spectral kurtosis. For very high speed bearings, the impulse responses elicited by the sharp impacts in the bearings may have a comparable length to their separation, and the minimum entropy deconvolution technique may be found useful to remove the smearing effects of the (unknown) transmission path. The final diagnosis is based on “envelope analysis” of the optimally filtered signal, but despite the fact that this technique has been used for 40 years in analogue form, the advantages of more recent digital implementations are explained.
TL;DR: Business issues that need to be considered when selecting an appropriate modelling approach for trial are discussed and classification tables and process flow diagrams are presented to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment.
Abstract: Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.
TL;DR: A basic introduction to the most important procedures of computational model updating is provided, including tutorial examples to reinforce the reader’s understanding and a large scale model updating example of a helicopter airframe.
Abstract: The sensitivity method is probably the most successful of the many approaches to the problem of updating finite element models of engineering structures based on vibration test data. It has been applied successfully to large-scale industrial problems and proprietary codes are available based on the techniques explained in simple terms in this article. A basic introduction to the most important procedures of computational model updating is provided, including tutorial examples to reinforce the reader’s understanding and a large scale model updating example of a helicopter airframe.
TL;DR: In this article, a tutorial on Hilbert transform applications to mechanical vibration is presented, with a large number of examples devoted to illustrating key concepts on actual mechanical signals and demonstrating how the Hilbert transform can be taken advantage of in machine diagnostics, identification of mechanical systems and decomposition of signal components.
Abstract: This paper is a tutorial on Hilbert transform applications to mechanical vibration. The approach is accessible to non-stationary and nonlinear vibration application in the time domain. It thrives on a large number of examples devoted to illustrating key concepts on actual mechanical signals and demonstrating how the Hilbert transform can be taken advantage of in machine diagnostics, identification of mechanical systems and decomposition of signal components.
TL;DR: In this paper, the authors proposed a method based on the kurtosis of the envelope spectrum amplitudes of the demodulated signal, rather than on the filter time signal, to detect transients with smaller signal-to-noise ratio comparing to the spectral kurtogram.
Abstract: The narrowband amplitude demodulation of a vibration signal enables the extraction of components carrying information about rotating machine faults. However, the quality of the demodulated signal depends on the frequency band selected for the demodulation. The spectral kurtosis (SK) was proved to be a very efficient method for detection of such faults, including defective rolling element bearings and gears  . Although there are conditions, under which SK yields valid results, there are also cases, when it fails, e.g. in the presence of a relatively strong, non-Gaussian noise containing high peaks or for a relatively high repetition rate of fault impulses. In this paper, a novel method for selection of the optimal frequency band, which attempts to overcome the aforementioned drawbacks, is presented. Subsequently, a new tool for presentation of results of the method, called the Protrugram, is proposed. The method is based on the kurtosis of the envelope spectrum amplitudes of the demodulated signal, rather than on the kurtosis of the filtered time signal. The advantage of the method is the ability to detect transients with smaller signal-to-noise ratio comparing to the SK-based Fast Kurtogram. The application of the proposed method is validated on simulated and real data, including a test rig, a simulated signal, and a jet engine vibration signal.
TL;DR: In this article, the authors investigated failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE) signals collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB), and four-point bend End Notched Flexure (4-ENF).
Abstract: This paper investigates failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE). Signals have been collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB) and four-point bend End Notched Flexure (4-ENF). The signals are analysed with three different pattern recognition algorithms: k-means, Self Organising Map (SOM) combined with k-means and Competitive Neural Network (CNN). The SOM combined with k-means appears as the most effective of the three algorithms. The results from the clustering analysis follow patterns found in the peak frequencies distribution. A detailed study of the frequency content of each test is then performed and the classification of several failure modes is achieved.
TL;DR: Wang et al. as mentioned in this paper proposed an improved kurtogram method adopting wavelet packet transform (WPT) as the filter of kurtograms to overcome the shortcomings of the original Kurtogram, which can filter out noise and precisely match the fault characteristics of noisy signals.
Abstract: Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery fault diagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.
TL;DR: In this paper, the authors present some preliminary results for the test/analysis/correlation of data measured using the 3D digital image correlation (DIC) approach along with traditional accelerometers and a scanning laser vibrometer for comparison to a finite element model.
Abstract: In the area of modal test/analysis/correlation, significant effort has been expended over the past twenty years in order to make reduced models and to expand test data for correlation and eventual updating of the finite element models. This has been restricted by vibration measurements which are traditionally limited to the location of relatively few applied sensors. Advances in computers and digital imaging technology have allowed 3D digital image correlation (DIC) methods to measure the shape and deformation of a vibrating structure. This technique allows for full-field measurement of structural response, thus providing a wealth of simultaneous test data. This paper presents some preliminary results for the test/analysis/correlation of data measured using the DIC approach along with traditional accelerometers and a scanning laser vibrometer for comparison to a finite element model. The results indicate that all three approaches correlated well with the finite element model and provide validation for the DIC approach for full-field vibration measurement. Some of the advantages and limitations of the technique are presented and discussed.
TL;DR: In this article, a comparison of three different model-based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies, is presented.
Abstract: This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.
TL;DR: Reading this tutorial is expected to get a flavour of what moving-load problems are about, what general methods are available and what research has been done from studying this tutorial.
Abstract: This tutorial is dedicated to the study of structural dynamics problems caused by moving loads. Through a simple example of a simply supported beam traversed by a moving mass, several fundamental concepts peculiar to moving-load problems are introduced. The necessary mathematics involved is presented. The analytical procedure is also presented for a circular plate excited by a rotating oscillator. Then numerical results of a circular beam spinning about its longitudinal axis excited by an axially moving surface load are provided. A variety of moving-load problems are briefly reviewed with some published papers and books to help readers quickly get into problems of their interests. Readers are expected to get a flavour of what moving-load problems are about, what general methods are available and what research has been done from studying this tutorial. Knowledge of partial differential equations and vibration theory of beams and plates is required in order to understand this tutorial.
TL;DR: A comprehensive overview of various radio channel access protocols and resource management approaches are provided, and their suitability for infotainment and safety service support in VANETs is discussed.
Abstract: Vehicular ad hoc networking is an emerging technology for future on-the-road communications. Due to the virtue of vehicle-to-vehicle and vehicle-to-infrastructure communications, vehicular ad hoc networks (VANETs) are expected to enable a plethora of communication-based automotive applications including diverse in-vehicle infotainment applications and road safety services. Even though vehicles are organized mostly in an ad hoc manner in the network topology, directly applying the existing communication approaches designed for traditional mobile ad hoc networks to large-scale VANETs with fast-moving vehicles can be ineffective and inefficient. To achieve success in a vehicular environment, VANET-specific communication solutions are imperative. In this paper, we provide a comprehensive overview of various radio channel access protocols and resource management approaches, and discuss their suitability for infotainment and safety service support in VANETs. Further, we present recent research activities and related projects on vehicular communications. Potential challenges and open research issues are also discussed.
TL;DR: In this article, wavelet transform tools are considered as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored.
Abstract: Time–frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.
TL;DR: The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research.
Abstract: A great many computational algorithms developed over the past half-century have been motivated or suggested by biological systems or processes, the most well-known being the artificial neural networks. These algorithms are commonly grouped together under the terms soft or natural computing. A property shared by most natural computing algorithms is that they allow exploration of, or learning from, data. This property has proved extremely valuable in the solution of many diverse problems in science and engineering. The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research. The application of some of the main algorithms is illustrated using case studies. The paper also attempts to give some indication as to which of the algorithms emerging now from the machine learning community are likely to be important for mechanical systems research in the future.
TL;DR: In this paper, two approaches are proposed to enhance the entry event while keeping the impulse response in order to enable a clear separation of the two events, and produce an averaged estimate of the size of the fault.
Abstract: Fatigue in rolling element bearings, resulting in spalling of the races and/or rolling elements, is the most common cause of bearing failure. The useful life of the bearing may extend considerably beyond the appearance of the first spall and a premature removal of the bearing from service can be very expensive, but on the other hand chances cannot be taken with safety of machines or personnel. Previous studies indicated that there might be two parts to the defect vibration signal of a spalled bearing, the first part being originating from the entry of the rolling element into the fault (de-stress) and the second part being due to the departure of the rolling element from the fault (re-stress). This is investigated in this paper using vibration signatures of seeded faults at different speeds. The acceleration signals resulting from the entry of the rolling element into the spall and exit from it were found to be of different natures. The entry into the fault can be described as a step response, with mainly low frequency content, while the impact excites a much broader frequency impulse response. The latter is the most noticeable and prominent event, especially when examining the high pass filtered response or the enveloped signal. In order to enable a clear separation of the two events, and produce an averaged estimate of the size of the fault, two approaches are proposed to enhance the entry event while keeping the impulse response. The first approach (joint treatment) utilizes pre-whitening to balance the low and high frequency energy, then octave band wavelet analysis to allow selection of the best band (or scale) to balance the two pulses with similar frequency content. In the second approach, a separate treatment is applied to the step and the impulse responses, so that they can be equally represented in the signal. Cepstrum analysis can be used to give an average estimate of the spacing between the entry and impact events, but the latter can also be assessed by an arithmetic estimation of the mean and standard deviation of the event separation for a number of realizations, in particular for the second approach. In order to determine the effects of various simulations and signal processing parameters on the estimated delay times, the entry and exit events were simulated as modified step and impulse responses with precisely known starting times. The simulation was also found useful in pointing to artefacts associated with the cepstrum calculation, which affect even the simulated signals, and have thus prompted modifications of the processing of real signals. The results presented for the two approaches give a reasonable approximation of the measured fault widths (double the spacing between the entry and impact events) under different speed conditions, but the method of separate treatment is somewhat better and is thus recommended.
TL;DR: The experimental results indicate potential applications of LPP-based FE and Gaussian mixture model (GMM)-based negative log likelihood probability (NLLP) as effective tools for bearing performance degradation assessment.
Abstract: The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance prediction without human intervention. This paper proposes a locality preserving projections (LPP)-based FE approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The effectiveness of the proposed approach for bearing defect and severity classification is evaluated experimentally on bearing test-beds. Furthermore, a novel health assessment indication, Gaussian mixture model (GMM)-based negative log likelihood probability (NLLP) is developed to provide a comprehensible indication for quantifying bearing performance degradation. The proposed approach has shown to provide better performance than using regular features (e.g., root mean square (RMS)). The experimental results indicate potential applications of LPP-based FE and GMM as effective tools for bearing performance degradation assessment.
TL;DR: In this paper, a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University was used for structural health monitoring, and several nonlinear models were proposed to represent the relationship between the identified natural frequencies and measured temperatures.
Abstract: Continuous monitoring of structural vibrations is becoming increasingly common as sensors and data acquisition systems become more affordable, and as system and damage identification methods develop. In vibration-based structural health monitoring, the dynamic modal parameters of a structure are usually used as damage-sensitive features. The modal parameters are often sensitive to changing environmental conditions such as temperature, humidity, or excitation amplitude. Environmental conditions can have as large an effect on the modal parameters as significant structural damage, so these effects should be accounted for before applying damage identification methods. This paper presents results from a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University. Significant variability in the identified natural frequencies is observed; these changes in natural frequency are strongly correlated with temperature. Several nonlinear models are proposed to represent the relationship between the identified natural frequencies and measured temperatures. The final model is then validated using independent sets of measured data. Finally, confidence intervals are estimated for the identified natural frequencies as a function of temperature. The ratio of observed outliers to the expected rate of outliers based on the confidence level can be used as a damage detection index.
TL;DR: In this article, the authors present a literature review pertaining to joint characteristics, types of joint models, and briefly examines models used in the simulation of assembled structures, including node-to-node contact using a Jenkins friction model, thin layer elements, and zero thickness elements.
Abstract: This article gives an overview of different approaches for modeling the dynamics of mechanical joints in assembled structures. It contains a literature review pertaining to joint characteristics, types of joint models, and briefly examines models used in the simulation of assembled structures. Further on, a more detailed insight in joint modeling with the finite element method based on three different approaches is given: node-to-node contact using a Jenkins friction model, thin layer elements, and zero thickness elements. These approaches presented with real life applications come from an extensive joint modeling research performed at the Institute of Applied and Experimental Mechanics in the last 15 years. The advantages as well as limitations of these methods are discussed and the authors give practical hints for which cases these methods can be implemented. The article is supplemented with easy to model examples, so that the interested reader can apply the proposed approaches and compare the results with solutions provided by the authors.
TL;DR: In this paper, the authors introduce the topic of operational modal analysis to non-specialists on the subject and present three of the most powerful algorithms for output-only modal identification.
Abstract: This tutorial paper aims to introduce the topic of operational modal analysis to non-specialists on the subject. First of all, it is stressed the relevance of this experimental technique particularly in the assessment of important civil infrastructure. Then, after a synthesis of required theoretical background, three of the most powerful algorithms for output-only modal identification are presented. The several steps of these identification procedures are illustrated with the processing of data collected on a concrete arch bridge with a span of 280 m. As the use of operational modal analysis in the context of structural health monitoring is a subject under active research, this theme is also introduced and briefly exemplified with data continuously recorded at the same bridge.
TL;DR: A broad outlook on rotor fault monitoring techniques for the researchers and engineers can be found in this paper, where the authors review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection.
Abstract: Nowadays, manufacturing companies are making great efforts to implement an effective machinery maintenance program, which provides incipient fault detection. The machine problem and its irregularity can be detected at an early stage by employing a suitable condition monitoring accompanied with powerful signal processing technique. Among various defects occurred in machines, rotor faults are of significant importance as they cause secondary failures that lead to a serious motor malfunction. Diagnosis of rotor failures has long been an important but complicated task in the area of motor faults detection. This paper intends to review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection. The aim of this article is to provide a broad outlook on rotor fault monitoring techniques for the researchers and engineers.
TL;DR: In this article, a merit index is introduced that allows the automatic selection of the intrinsic mode functions that should be used for the calculation of the Hilbert-Huang spectrum of a spiral bevel gearbox.
Abstract: Signal processing is an important tool for diagnostics of mechanical systems. Many different techniques are available to process experimental signals, among others: FFT, wavelet transform, cepstrum, demodulation analysis, second order ciclostationarity analysis, etc. However, often hypothesis about data and computational efforts restrict the application of some techniques. In order to overcome these limitations, the empirical mode decomposition has been proposed. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert–Huang spectrum. Anyhow, the selection of the intrinsic mode functions used for the calculation of Hilbert–Huang spectrum is normally done on the basis of user’s experience. On the contrary, in the paper a merit index is introduced that allows the automatic selection of the intrinsic mode functions that should be used. The effectiveness of the improvement is proven by the result of the experimental tests presented and performed on a test-rig equipped with a spiral bevel gearbox, whose high contact ratio made difficult to diagnose also serious damages of the gears. This kind of gearbox is normally never employed for benchmarking diagnostics techniques. By using the merit index, the defective gearbox is always univocally identified, also considering transient operating conditions.
TL;DR: In this article, a signal analysis geared to periodic signals was introduced, with the potential of extracting more complex phenomena typical of some rotating machinery, such as periodic oscillating transients, with various additive interferences.
Abstract: Synchronous averaging is one of the most powerful techniques for the extraction of periodic signals from a composite signal. It is based on averaging periodic sections, necessitating an a-priori knowledge of the period sought. It is one of the most effective signal processing tools applied to rotating machinery, and has been known and used for decades.It will be shown that synchronous average is actually just one of the many possible "synchronous filters" which could be used to extract the above periodic components performance. A novel signal analysis, geared to periodic signals will be introduced, with the potential of extracting more complex phenomena typical of some rotating machinery. Examples given are based on periodic oscillating transients, with various additive interferences. The possibility of additional signal processing approaches is also discussed.
TL;DR: In this article, an adaptive spectral kurtosis (SK) technique was proposed for the fault detection of rolling element bearings, which is implemented with successive attempts to rightexpand a given window along the frequency axis by merging it with its subsequent neighboring windows.
Abstract: In this paper, we propose an adaptive spectral kurtosis (SK) technique for the fault detection of rolling element bearings. The primary contribution is adaptive determination of the bandwidth and center frequency. This is implemented with successive attempts to right-expand a given window along the frequency axis by merging it with its subsequent neighboring windows. Influence of the parameters such as the initial window function, bandwidth and window overlap on the merged windows as well as how to choose those parameters in practical applications are explored. Based on simulated experiments, it can be found that the proposed technique can further enhance the SK-based method as compared to the kurtogram approach. The effectiveness of the proposed method in fault detection of the rolling element bearings is validated using experimental signals.
TL;DR: In this article, three on-line monitoring techniques are implemented in the tests and data fusion is accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix.
Abstract: The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and nonconventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes. The integration of vibration, AE and ODM data increases the diagnostic capacity and reliability of the condition monitoring scheme concluding to very interesting results. The present work summarizes the joint efforts of two research groups towards a more reliable condition monitoring of rotating machinery and gearboxes specifically.
TL;DR: Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection in this paper, and the proposed method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period.
Abstract: At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection. With the proposed method, both parameters of a single transient and the period between transients can be identified from the vibration signal, and localized faults can be detected based on the parameters, especially the period. First, a simulation signal is used to test the performance of the proposed method. Then the method is applied to the vibration signals of different types of bearings with localized faults in the outer race, the inner race and the rolling element, respectively, and all the results show that the period between transients, representing the localized fault characteristic, is successfully detected. The method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period. Moreover, it can be drawn that for bearing fault detection, the single-side wavelet model is more suitable than double-side one, while the double-side model for gearbox fault detection. This research proposed an effective method of localized fault detection for rotating machine fault diagnosis through transient modeling and parameter detection.
TL;DR: In this paper, the Kriging predictor is chosen as the meta-model and is found to be capable of predicting the regions of input and output parameter variations with very good accuracy, which enables the use of updating parameters that are difficult to use by conventional correction of the finite element model.
Abstract: Interval model updating in the presence of irreducible uncertain measured data is defined and solutions are made available for two cases. In the first case, the parameter vertex solution is used but is found to be valid only for particular parameterisation of the finite element model and particular output data. In the second case, a general solution is considered, based on the use of a meta-model which acts as a surrogate for the full finite element mathematical model. Thus, a region of input data is mapped to a region of output data with parameters obtained by regression analysis. The Kriging predictor is chosen as the meta-model in this paper and is found to be capable of predicting the regions of input and output parameter variations with very good accuracy. The interval model updating approach is formulated based on the Kriging predictor and an iterative procedure is developed. The method is validated numerically using a three degree of freedom mass-spring system with both well-separated and close modes. A significant advantage of Kriging interpolation is that it enables the use of updating parameters that are difficult to use by conventional correction of the finite element model. An example of this is demonstrated in an experimental exercise where the positions of two beams in a frame structure are selected as updating parameters.
TL;DR: The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it.
Abstract: In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses; a redundant dictionary is built by merging all the learned basis functions; based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals; sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis.
TL;DR: In this article, the applicability of Acoustic Emission (AE) and vibration technologies in monitoring a naturally degraded roller bearing has been investigated and the first known attempt investigating the comparative effectiveness of applying the Kurtogram to both vibration and AE data from a defective bearing.
Abstract: The application of Acoustic Emission (AE) technology for machine health monitoring is gaining ground as power tool for health diagnostic of rolling element bearing. This paper provides an investigation that compares the applicability of AE and vibration technologies in monitoring a naturally degraded roller bearing. This research is the first known attempt investigating the comparative effectiveness of applying the Kurtogram to both vibration and AE data from a defective bearing.
TL;DR: In this paper, a non-linear multivariate and multiscale statistical process monitoring and signal denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) nonlinear multi-linear monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) is presented.
Abstract: The article presents a novel non-linear multivariate and multiscale statistical process monitoring and signal denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) non-linear multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) to handle multiscale system dynamics. The proposed method which enables us to cope with complex even severe non-linear systems with a wide dynamic range was named the EEMD-based multiscale KPCA (EEMD-MSKPCA). The method is quite general in nature and could be used in different areas for various tasks even without any really deep understanding of the nature of the system under consideration. Its efficiency was first demonstrated by an illustrative example, after which the applicability for the task of bearing fault detection, diagnosis and signal denosing was tested on simulated as well as actual vibration and acoustic emission (AE) signals measured on purpose-built large-size low-speed bearing test stand. The positive results obtained indicate that the proposed EEMD-MSKPCA method provides a promising tool for tackling non-linear multiscale data which present a convolved picture of many events occupying different regions in the time–frequency plane.
TL;DR: In this paper, the authors proposed the concept of difference spectrum of singular value, which consists of the forward differences of the singular value sequence and can describe the sudden change status of singular values of a complicated signal.
Abstract: The noise reduction effect of singular value decomposition (SVD) relies on the selection of effective singular values. The characteristic of singular values of normal signal and noise being studied, it is pointed out that there is a sudden change in the singular values of normal signal, but not in the ones of noise. The concept of difference spectrum of singular value is put forward, which consists of the forward differences of singular value sequence and can describe the sudden change status of singular values of a complicated signal. The automatic selection of effective singular values can be realized by the peak of the difference spectrum. If the maximum peak of difference spectrum is located in the first coordinates, it means that a strong direct current (DC) component is contained in original signal and the number of effective singular values will be determined by the second maximum peak coordinates, while what the first singular value corresponds to is the DC component, or else the number of effective singular values is determined by the maximum peak coordinates. The relationship between column number of matrix and noise removing quantity of SVD is also studied using difference spectrum and the result shows that this relationship is like a symmetrical parabola. By dint of the difference spectrum, the hidden modulation feature caused by gear vibration in headstock is isolated from a turning force signal and the fault gear is accurately located by this modulation feature.
TL;DR: In this paper, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults in machining processes and rotating machinery, which is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults.
Abstract: Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.