Showing papers on "Condition monitoring published in 2015"
TL;DR: In this article, a mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented, and the chosen features are used to train an artificial neural network (ANN) to classify bearing defects.
594 citations
TL;DR: An analysis of the state of the art in this field of electrical machines and drives condition monitoring and fault diagnosis is presented.
Abstract: Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.
441 citations
TL;DR: This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines (WTs) and discusses the common failure modes in the major WT components and subsystems.
Abstract: This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines (WTs). The Part I of this survey briefly reviews the existing literature surveys on the subject, discusses the common failure modes in the major WT components and subsystems, briefly reviews the condition monitoring and fault diagnostic techniques for these components and subsystems, and specifically discusses the issues of condition monitoring and fault diagnosis for offshore WTs.
402 citations
TL;DR: Practical engineering solutions are focused on which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies, which identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.
Abstract: In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile ad hoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally, which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review.
392 citations
TL;DR: In this article, the authors provide a critical review of the predictive health monitoring methods of the entire defect evolution process i.e. wear evolution over the whole lifetime and suggest enhancements for rolling element bearing monitoring.
346 citations
TL;DR: In this paper, the authors summarize past developments and recent advances in the area of condition monitoring and prognostics for IGBT modules and provide recommendations for future research topics in the CM and prognostic areas.
Abstract: Recent growth of the insulated gate bipolar transistor (IGBT) module market has been driven largely by the increasing demand for an efficient way to control and distribute power in the field of renewable energy, hybrid/electric vehicles, and industrial equipment. For safety-critical and mission-critical applications, the reliability of IGBT modules is still a concern. Understanding the physics-of-failure of IGBT modules has been critical to the development of effective condition monitoring (CM) techniques as well as reliable prognostic methods. This review paper attempts to summarize past developments and recent advances in the area of CM and prognostics for IGBT modules. The improvement in material, fabrication, and structure is described. The CM techniques and prognostic methods proposed in the literature are presented. This paper concludes with recommendations for future research topics in the CM and prognostics areas.
341 citations
TL;DR: Results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their RUL, and can be used by the operators to take appropriate maintenance actions.
Abstract: The integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool's one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life (RUL) of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their RUL. This information can then be used by the operators to take appropriate maintenance actions.
305 citations
TL;DR: This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines.
Abstract: This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines. The Part II of this survey focuses on the signals and signal processing methods used for wind turbine condition monitoring and fault diagnosis.
301 citations
TL;DR: A self-evolving maintenance scheduler framework for maintenance management of wind turbines is introduced and an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed.
Abstract: Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.
288 citations
TL;DR: In this article, a focus of condition monitoring is to detect partial discharge (PD) especially in the early stages to prevent a serious power failure or outage, which is a key indicator of such electrical failure.
Abstract: As one step toward the future smart grid, condition monitoring is important to facilitate the reliability of grid asset operation and to save on maintenance cost [1]. Most failures of the power grid are caused by electrical insulation failure, and a key indicator of such electrical failure is the occurrence of partial discharge (PD). Therefore, one focus of condition monitoring is to detect PD, especially in the early stages, to prevent a serious power failure or outage.
203 citations
TL;DR: On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to hydraulic brakes problems by using the decision tree algorithm.
TL;DR: A novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier, which indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.
Abstract: Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed The average of these performance measures is computed to report the overall performance of the support vector machine classifier In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity The sensitivity and robustness of the proposed method are explored by running a series of experiments A receiver operating characteristic (ROC) curve made the results more convincing The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals
TL;DR: A view of the current state of monitoring track geometry condition from in-service vehicles is presented, which considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research.
Abstract: This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feedin...
TL;DR: A new data-driven prognostics approach namely, an “enhanced multivariate degradation modeling,” which enables modeling degrading states of machinery without assuming a homogeneous pattern is contributed.
Abstract: Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an “enhanced multivariate degradation modeling,” which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.
11 May 2015
TL;DR: A systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated and the classification rate for random load cycles was enhanced by a distribution analysis of feature trends.
Abstract: In this paper, a systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated. We analyzed different fault scenarios using a test rig that allows simulating a reversible degradation of component's conditions. By analyzing the correlation of features extracted from raw sensor data and the known fault characteristics of experimental obtained data, the most significant features specific to a fault case can be identified. These feature values are transferred to a lower-dimensional discriminant space using linear discriminant analysis (LDA) which allows the classification of fault condition and grade of severity. We successfully implemented and tested the system for a fixed working cycle of the hydraulic system. Furthermore, the classification rate for random load cycles was enhanced by a distribution analysis of feature trends.
TL;DR: In this article, a review of work presented by various researchers on instruments used for vibration measurement and signal processing techniques for condition monitoring of machine tools in manufacturing operations is presented, which can be used to detect the nature and extent of any damage in machines and components or any maintenance decisions related to the machine.
Abstract: During the operation, machines generate vibrations which result in deterioration of machine tools eventually causing failure of some subsystems or the machine itself. The vibration signatures analysis can be used to detect the nature and extent of any damage in machines and components or any maintenance decisions related to the machine. The condition based monitoring has become an important technique to ensure the machine availability by timely maintenance actions and reducing breakdown maintenance. This paper presents the review of work presented by various researchers on instruments used for vibration measurement and signal processing techniques for condition monitoring of machine tools in manufacturing operations.
TL;DR: A novel algorithm for CBM based on SDA that takes advantage of the Online Support Vector Regression (OL-SVR) for predicting the RUL is proposed, which is the heuristic approach for optimizing the trade-off between the accuracy of the OL-S VR models and the computational time and resources needed in order to build them.
TL;DR: A novel automatic fault detection system using infrared imaging, focussing on bearings of rotating machinery, able to distinguish between all eight different conditions with an accuracy of 88.25%.
TL;DR: In this paper, a simplified nonlinear gear model is developed, on which the time-frequency analysis method is first applied for the easiest understanding of the challenges faced, and the effect of varying loads is examined in the simulations and later on in real wind turbine gearbox experimental data.
TL;DR: A combined automatic method is proposed to detect very small defects on roller bearings and it is shown that the combined method proposed is able to identify the states of the bearings effectively.
Abstract: Roller bearings are widely used in rotating machinery and one of the major reasons for machine breakdown is their failure. Vibration based condition monitoring is the most common method for extracting some important information to identify bearing defects. However, acquired acceleration signals are usually noisy, which significantly affects the results of fault diagnosis. Wavelet packet decomposition (WPD) is a powerful method utilized effectively for the denoising of the signals acquired. Furthermore, Ensemble empirical mode decomposition (EEMD) is a newly developed decomposition method to solve the mode mixing problem of empirical mode decomposition (EMD), which is a consequence of signal intermittence. In this study a combined automatic method is proposed to detect very small defects on roller bearings. WPD is applied to clean the noisy signals acquired, then informative feature vectors are extracted using the EEMD technique. Finally, the states of the bearings are examined by labeling the samples using the hyperplane constructed by the support vector machine algorithm. The data were generated by means of a test rig assembled in the labs of the Dynamics and Identification Research Group in the mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and a very small damage size on a roller) were considered to obtain reliable results. It is shown that the combined method proposed is able to identify the states of the bearings effectively.
TL;DR: In this article, the authors proposed a procedure for the parameterized TFA to analyze the non-stationary vibration signal of varying-speed rotary machinery, which is used for fault detection, system condition monitoring, parameter identification, etc.
TL;DR: The applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective are evaluated and the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes are reviewed.
Abstract: The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.
TL;DR: This letter proposes a novel condition monitoring scheme of dc-link capacitors in PWM inverter-fed induction machine drives with front-end diode rectifiers, which is based on the online capacitance estimation scheme, and results have shown that the estimation error of the capacitance is less than 1%, from which the deterioration condition of the capacitor can be diagnosed reliably.
Abstract: This letter proposes a novel condition monitoring scheme of dc-link capacitors in PWM inverter-fed induction machine drives with front-end diode rectifiers, which is based on the online capacitance estimation scheme. While the motor is operating in the regenerative mode for the estimation process, a regulated ac component is injected into the stator winding, which causes a dc-link voltage ripple at the same frequency. From the ac components of the dc-link voltage and current, the capacitance is estimated with a recursive least squares algorithm. With this method, experimental results have shown that the estimation error of the capacitance is less than 1%, from which the deterioration condition of the capacitors can be diagnosed reliably.
TL;DR: Results showed that the developed SKF approach is a promising tool to support maintenance decision-making and the inference of the degradation model itself can provide maintainers with more information for their planning.
TL;DR: The process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised is addressed.
TL;DR: This paper proposes how to use the addition of rotor position offsets as perturbation signals for the parameter estimation of permanent-magnet synchronous machines (PMSMs), which can be used for the condition monitoring of rotor permanent magnet and stator winding.
Abstract: This paper proposes how to use the addition of rotor position offsets as perturbation signals for the parameter estimation of permanent-magnet synchronous machines (PMSMs), which can be used for the condition monitoring of rotor permanent magnet and stator winding. During the proposed estimation, two small position offsets are intentionally added into the drive system, and the resulting voltage variation will be recorded for the estimation of rotor flux linkage. With the aid from estimated rotor flux linkage, the stator winding resistance can be subsequently estimated at steady state. This method is experimentally verified on two prototype PMSMs (150 W and 3 kW, respectively) and shows good performance in monitoring the variation of rotor flux linkage and winding resistance.
TL;DR: An approach for bearing fault prognostics that employs Renyi entropy based features that exploits the idea that progressing fault implicates raising dissimilarity in the distribution of energies across the vibrational spectral band sensitive to the bearing faults is proposed.
TL;DR: In this paper, the authors proposed an envelope analysis method for bearing incipient defect detection, which is able to identify localized defects in an incipient stage, in which the signal-to-noise ratio (SNR) is extremely low.
TL;DR: In this paper, a model-based condition monitoring strategy is developed for lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect, which systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least square algorithm for estimating the maximum capacity, which indicates the SOH.
13 Apr 2015
TL;DR: A new WWSN for anomaly detections of health conditions has been proposed, system architecture and network has been discussed, the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.
Abstract: Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task, which involves anatomy, artificial intelligence, and sensing and computing networks. A wearable wireless sensor network (WWSN) turns into an emerging technology, which is capable of acquiring dynamic data related to a human body's physiological conditions. The collected data can be applied to detect anomalies in a patient, so that he or she can receive an early alert about the adverse trend of the health condition, and doctors can take preventive actions accordingly. In this paper, a new WWSN for anomaly detections of health conditions has been proposed, system architecture and network has been discussed, the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN. The novelty of the detected model lies in its relevance to chronobiology. Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of t...