Showing papers on "Condition monitoring published in 2023"

Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network

Abstract: The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.

Edge-Intelligence-Based Condition Monitoring of Beam Pumping Units Under Heavy Noise in Industrial Internet of Things for Industry 4.0

Abstract: Accurately estimating the state of equipment plays an important role in ensuring the efficient operation of Industrial 4.0 systems. This article focuses on monitoring the operating state and detecting the faults of beam pumping units under the condition of heavy noise within the Industrial Internet of Things. On the one hand, the equipment operating state monitoring system designed in this article uses an acceleration sensor, the signal of which contains considerable noise that greatly reduces the motion state estimation accuracy. On the other hand, the complexity of the indicator diagrams of beam pumping units makes it difficult to extract features, which limits the ability to improve the fault detection accuracy. To overcome these issues, first, a period estimation method based on self-checking that employs acceleration data is proposed to effectively overcome the influence of complex noise on the estimated data period; second, a denoising method based on a physical model is proposed to effectively reduce the influence of complex noise on the acceleration-based displacement estimation; and third, a method for detecting the faults of beam pumping units based on edge intelligence is proposed to effectively improve the fault detection accuracy while maintaining a low computational demand. Extensive experiments on real data verify the effectiveness of the proposed method. To the best of our knowledge, this is the first work to discuss the impact of the quality of data on the performance of fault detection of beam pump units.

Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis

Abstract: Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of expenditure. SCADA-based condition monitoring for anomaly detection is commonly presented as a convenient solution for fault diagnosis on turbine components. However, its suitability is generally proven by empirical analyses which are limited in time and based on a circumscribed number of turbines. To cope with this lack of validation, this paper performs a controlled experiment to evaluate the suitability of SCADA-based condition monitoring for fault diagnosis in a fleet of eight turbines monitored for over 11 years. For the controlled experiment, a weakly supervised method was used to model the normal behavior of the turbine component. Such a model is instantiated as a convolutional neural network. The method, instantiated as a threshold-based method, proved to be suitable for diagnosis, i.e. the identification of all drivetrain failures with a considerable advance time. On the other hand, the wide variability between the time the alarm is raised and the fault is observed suggests its limited suitability for prognosis.

Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions

Abstract: Tool breakage occurs randomly during machining operations, which induces more severe impacts on the quality of components compared to progressive tool wear. It is widely acknowledged that the unpredictable changes in cutting conditions will cause fluctuations in the signal amplitude and thus generate false alarms. This study introduced a novel method for tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions. The amplitude ratio (AR) and the energy ratio (ER) were proposed according to the power spectrum of the spindle vibration signal, which represents the change of amplitude and the energy distribution, respectively. The AR and ER are normalized and integrated into a unified indicator for real-time breakage monitoring. The floating monitoring threshold is designed based on the Gaussian distribution. Moreover, the material removal rate (MRR) is selected as a secondary indicator to accurately identify tool breakage based on determining the amplitude fluctuation caused by cutting conditions or teeth breakage. The effectiveness of the proposed method for tool breakage monitoring has been verified under the constant, time-varying, and entry/exit cutting conditions. The results show that the proposed indicators have higher sensitivity than the traditional root mean square (RMS) features and eliminate false alarms during condition change transients. This research provides a potential solution for tool breakage monitoring under complex cutting conditions.

Condition Monitoring using Machine Learning: Theory and Applications

Abstract: In modern industry, the quality of maintenance directly influences equipment’s operational uptime and efficiency. Hence, based on monitoring the condition of the machinery, predictive maintenance can minimize machine downtime and potential losses. Throughout the field, machine learning (ML) methods have become noteworthy for predicting failures before they occur. However, the efficacy of the predictive maintenance strategy relies on selecting the appropriate data processing method and ML model. Existing surveys do not comprehensively inform users or evaluate the quality of the monitoring systems proposed. Hence, this survey reviews the recent literature on ML-driven condition monitoring systems that have been beneficial in many cases. Furthermore, in the reviewed literature, we provide an insight into the underlying findings on successful, intelligent condition monitoring systems. It is prudent to consider all factors when narrowing the search for the most effective model for a particular task. Therefore, the tradeoff between task constraints and the performance of each diagnostic technique are quantitively and comparatively evaluated to obtain the given problem's optimal solution.

A Blind Deconvolution Approach Based on Spectral Harmonics-to-Noise Ratio for Rotating Machinery Condition Monitoring

Abstract: Harmonics-to-noise ratio (HNR) is an important health index of rotating machine, which has been applied in blind deconvolution (BD) method to realize periodic impulse detection. However, most fault impulses are not strictly periodic, but pseudo-cyclostationary, which will affect the performance of HNR in fault characterization to some extent. This limits its applications. Therefore, in this paper, a novel BD method, maximum squared envelope spectrum harmonic-to-interference ratio deconvolution (MSESHIRD), is proposed to more effectively achieve fault identification. The proposed method seeks a target filter by maximizing squared envelope spectrum harmonic-to-interference ratio (SESHIR). Since harmonic components corresponding to repetitive fault impulses in SES are less sensitive to random fluctuations, SESHIR can more accurately distinguish repetitive fault impulses from irrelevant interference in vibration signals. Therefore, BD based on SESHIR has better performance than BD based on HNR in measuring fault features in signals. Through simulation and experimental case analysis, the proposed method is compared with several public methods Results show that the proposed method has better performance in fault characteristic extraction. In addition, it is implemented on bearing run-to-failure data for condition monitoring to show that the proposed method has excellent ability of early fault detection. Note to Practitioners—This paper is motivated by the problems of automatic operating condition monitoring and early defect diagnosis of rotating machines. These problems can be effectively solved by designing a BD method based on reliable and efficient health indices. HNR defined on autocorrelation function (AF) is an excellent health index to characterize the signal-to-noise ratio (SNR) of repetitive fault impulse in signals. However, this paper uses mathematical models of HNR to show that it has very strict requirements on the period and SNR of fault impulse signals. A fluctuating fault period or a low SNR might make HNR unable to accurately estimate the energy of fault components in signals, thus weakening its performance in fault characterization. Compared with HNR, SESHIR has better fault characterization ability due to that SES can more accurately obtain the periodicity (frequency) and energy of fault components in signals. Therefore, this paper proposes a novel BD method based on SESHIR maximization for repetitive impulse monitoring. Its effectiveness and robustness are verified by both theoretical justification and experimental results.

LSTM-Based Condition Monitoring and Fault Prognostics of Rolling Element Bearings Using Raw Vibrational Data

Abstract: The 4.0 industry revolution and the prevailing technological advancements have made industrial units more intricate. These complex electro-mechanical units now aim to improve efficiency and increase reliability. Downtime of such essential units in the current competitive age is unaffordable. The paradigm of fault diagnostics is being shifted from conventional to proactive predictive approaches. As a result, Condition-based Monitoring and prognostics are now essential components of complex industrial systems. This research is focused on developing a fault prognostic system using Long Short-Term Memory for rolling element bearings because they are a critical component of industrial systems and have one of the highest fault frequencies. Compared to other research, feature engineering is minimized by using raw time series sensor data as an input to the model. Our model achieved the lowest root mean square error and outperformed similar research models where time domain, frequency domain, or time-frequency domain features were used as input to the model. Furthermore, using raw vibration data also enabled better generalization of the model. This has been confirmed by evaluating the performance of the developed model against vibration data generated by distinct sources, including hydro and wind power turbines.

A Novel IEPE AE-vibration-temperature Combined Intelligent Sensor for Defect Detection of Power Equipment

Abstract: Insulation and mechanical defects are the main reasons of the power equipment fault, the former could generate partial discharge (PD) which can be detected by acoustic emission (AE) sensor, the other one can be evaluated by vibration and temperature variation. However, the two defects are detected and analyzed independently in traditional detection, the importance of multi-signal interaction and fusion analysis is ignored. To realize multi-signal synchronous detection and associated analysis, and improve the accuracy and efficiency of state evaluation of power equipment, a novel integrated electronics piezoelectric (IEPE) AE-vibration-temperature combined intelligent sensor is proposed and developed in this paper. Reasonable structure design and components customization enable the sensor to acquire signals simultaneously. Meanwhile, integrated transducer electronic data sheet (TEDS) and data processing circuit intelligentize the combined sensor. The primary detection parameters of the sensor are acquired in the calibration experiments. In PD, vibration and temperature joint experiment, it is verified that the combined sensor is more sensitive and multifunctional. Besides, a new fusion analysis diagram, vibration-phase resolved partial discharge (V-PRPD), is proposed to characterise the correlation between PD and vibration.

Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis

Abstract: Electric induction motors are the type of motor most commonly operated in industry, and for this reason technologies that predict faults and reduce the corrective maintenance are of great interest. In this context, this paper presents a predictive maintenance tool of electric motors using the concepts of Digital Twin (DT) and Industrial Internet of Things (IIoT). The proposed system is innovative, as it monitors the motor current and temperature by means of sensors and a low-cost acquisition module, and these measurements are sent via Wi-Fi to a database. The concept of DT was leveraged by providing the measurements as inputs to a high-fidelity strongly-coupled model of the monitored monitor, using the Finite Element Method (FEM). The results obtained are satisfactory, because the sensors used presented acceptable errors that do not interfere with the reliability of the results. The computer simulation showed relative errors below 4% in the conductivity analysis and 10% in the temperature analysis. In addition, the simulation allows verifying the internal temperature of the motor, its resistive losses, and the intensity of the magnetic flux at each pole. It is worth pointing out that the internal analysis performed is only possible due to the combination of IIoT and computer simulations. Therefore, they allow a better diagnosis of the motor’s operational status and also a time estimate for the next maintenance service, thus being ideal for the industrial sector.

Review on Partial Discharge Diagnostic Techniques for High Voltage Equipment in Power Systems

Abstract: In modern power systems, condition based monitoring and diagnosis is essential to ensure the effective and reliable operation of different high voltage equipment (HVE). Compared to other monitoring techniques, partial discharges (PD) measurement is considered as a key method for assessing the insulation health condition. The benefits of PD condition monitoring of HVE can be extended by proper detection, identification, and interpretation of PD signal. Among both online and offline PD monitoring techniques, online PD monitoring is a very promising technique that assists in robust monitoring system which reduces the power failure incidents in power system components. Therefore, to understand recent developments and trends in theory and in practice, it is necessary to establish a holistic analysis of current online PD monitoring techniques for HVE in power systems. This paper presents an intensive literature review of current online PD monitoring techniques used for different high voltage electric components in power system. Finally, a smart PD monitoring techniques based on wireless sensor board is proposed. The proposed smart PD monitoring framework may be used to correctly estimate the insulation degradation in HVE and enhance the overall performance of power systems.

Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Abstract: Electrical signature–based technique, due to its non-intrusive nature, is very useful for condition monitoring of rotary machines. Major challenge is the dominance of line frequency in the current signature. The present study focused on decreasing this dominance of the line frequency by obtaining the residual signals through autoregressive modeling. The residual signals are used further to extract the health-related features. The recently developed weighted multi-scale fluctuation-based dispersion entropy features are extracted as health indicators. The extracted health indicators are used for classification of different types of planetary gearbox faults. The results reflect that the proposed methodology has the potential for diagnosing different types of planetary gearbox faults with acceptable accuracy values.

Rolling bearing condition monitoring method based on multi-feature information fusion

Abstract: In machining operations, the misalignment of the bearing assembly or imbalanced load often leads to deflection and failure of the tool spindle. The use of single feature information does not accurately monitor the complex working conditions. Considering this, this paper proposes a rolling bearing running condition monitoring method which is based on multiple feature information. Firstly, a multi-dimensional feature matrix is obtained by extracting the features of a single type of raw data in the time domain, frequency domain, and time-frequency domain, and then the dimensionality of the matrix is reduced by principal component analysis (PCA). An entropy weight improved the D-S(EWID-S) evidence theory is proposed. By updating the initial evidence source, and applying the Euclidean distance of the spatial centroid, the fusion results were evaluated. Finally, a test rig for eccentric bearing load operation is developed to obtain the vibration signals at two distinct locations and to confirm the proposed method. The test results show that the condition monitoring method based on the PCA and EWID-S evidence theory can effectively identify the bearing operating at different degrees of deflection. At the same time, by comparing with other improved D-S evidence theory methods, it is verified that this method has more advantages in information fusion and bearing condition monitoring.

Ball Screw Health Monitoring With Inertial Sensors

Abstract: In industrial applications, the mechanical wear on ball screw components can lead to a loss of positioning accuracy that reduces the operational reliability and reproducibility of production systems. Existing monitoring solutions are impractical for real industrial settings or are unable to provide quantifiable estimates of the magnitude of degradation. To address this, the proposed method strategically applies a two-phase data collection with inertial sensors to perform both health monitoring and fault magnitude estimation. The first, online phase offers a practical, nonintrusive means of monitoring the ball screw degradation during normal production operations. As deemed necessary by the first phase, the second, offline phase is implemented outside the production routine to physically quantify the detected fault. The combined methods offer a balanced approach that provides detailed information while still considering the requirements of a production environment. To validate the performance of this proposed strategy, a run-to-failure experiment was performed on a linear axis testbed. Validation results indicate that the method is a pragmatic and promising approach for incipient fault detection and absolute backlash error measurement in a linear axis.

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Abstract: Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Abstract: Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.

Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison

Abstract: In this paper, a diagnostic procedure for rotor bar faults in induction motors is presented, based on the Hilbert and discrete wavelet transforms. The method is compared with other procedures with the same data, which are based on time–frequency analysis, frequency analysis and time domain. The results show that this method improves the rotor fault detection in transient conditions. Variable speed drive applications are common in industry. However, traditional condition monitoring methods fail in time-varying conditions or with load oscillations. This method is based on the combined use of the Hilbert and discrete wavelet transforms, which compute the energy in a bandwidth corresponding to the maximum fault signature. Theoretical analysis, numerical simulation and experiments are presented, which confirm the enhanced performance of the proposed method with respect to prior solutions, especially in time-varying conditions. The comparison is based on quantitative analysis that helps in choosing the optimal trade-off between performance and (computational) cost.

An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm

Abstract: The condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still pose tremendous challenges for reliable and timely fault detection. To address such challenges, in this study, a condition monitoring approach was designed to detect early faults of wind turbines. Specifically, based on a GRU network with a self-attention mechanism, a SAGRU normal behavior model for wind turbines was constructed, which can learn temporal features and mine complicated nonlinear correlations within different status parameters. Additionally, based on the residual sequence obtained using a well-trained SAGRU, a binary segmentation changepoint detection algorithm (BinSegCPD) was introduced to automatically identify deterioration conditions in a wind turbine. A case study of a main bearing fault collected from a 50 MW windfarm in southern China was employed to evaluate the proposed method, which validated its effectiveness and superiority. The results showed that the introduction of a self-attention mechanism significantly enhanced the model performance, and the adoption of a changepoint detection algorithm improved detection accuracy. Compared to the actual fault time, the proposed approach could automatically identify the deterioration conditions of main bearings 72.47 h in advance.

Condition monitoring of rotating electrical machines

Abstract: This article introduces the fundamentals and importance of electric machine condition monitoring to help minimize downtime and maximize lifespan. An overview of the different types of faults encountered during electric machine operation and their characteristics from a diagnostic perspective is first presented. This is followed by a general review of the leading condition monitoring techniques presently utilized and researched for electric machine applications. Condition monitoring of electric machines is becoming increasingly important with the rise in deployment of electric machines into critical industrial applications such as electric vehicles, aerospace systems, medical equipment and similar, where a failure could be catastrophic. Therefore the utilization of condition monitoring systems to monitor and track the health of an electric machine in operation is of vital importance. This article provides an introduction to the electric machine in terms of its fundamental operation and constituent parts. This is followed by an explanation of the main types of faults encountered in electric machines. Finally, a general description of the different types of condition monitoring techniques presently utilized to monitor electric machines in service is provided.

State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

Abstract: In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

Adversarial Representation Learning for Intelligent Condition Monitoring of Complex Machinery

Abstract: Condition monitoring (CM) of machinery is important for ensuring the reliability of industrial processes. To adapt to the rareness of data from faulted machinery, semisupervised CM can be implemented by training on only healthy samples. However, the performance of CM can be impaired by the variability of operating data acquired from complex machinery. Additionally, the accuracy of results is limited by the impractical assumption that samples under different health conditions are naturally separable. To address these problems, an adversarial representation learning method is developed herein. The method is trained by reconstructing operating data in both signal and latent spaces, and adversarial evolution is adopted to avoid the convergence at local optima. In this case, data representations of health conditions can be obtained to suppress the volatility of measurements, and redundant information can be reduced by latent codes. Moreover, a strategy of representation embedding is developed to impose constraints on unhealthy data, guaranteeing separable samples under distinct health conditions in the monitoring stage. Furthermore, feature fusion is conducted to avoid missing detailed information on health conditions. The satisfactory performance of the proposed method is demonstrated by experiments in test benches and actual scenarios of wind power generation.

Oil Monitoring and Fault Pre-Warning of Wind Turbine Gearbox Based on Combined Predicting Method

Abstract: Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings inaccuracy due to the intrinsic merits and demerits of the models. In this work, oil monitoring and fault pre-warning of wind turbine gearboxes were studied based on oil inspection data of three wind turbines that have been working continuously for 3.5 years. The Grey Model (GM) and the Double Exponential Smoothing (DES) were combined by a modified inverse-variance weighting method proposed in this work, which used relative errors to calculate weight coefficients, reducing the errors and improving the accuracy as a whole. The predicted data were compared with the measured data to verify the predicting accuracy. Subsequently, a statistical method and linear regression method were adopted to jointly develop a pre-warning threshold for the oil inspection data. Comparing the predicted data with the threshold, the results showed that one of the wind turbines was in a warning state. The prediction was validated by an endoscope inspection of the gearbox, which found that some parts were slightly worn.

Condition-Based Maintenance Optimization Method Using Performance Margin

Abstract: As a maintenance strategy to reduce unexpected failures and enable safe operation, condition-based maintenance (CBM) has been widely used in recent years. The maintenance decision criteria of CBM in the literature mostly originate from statistical failure data or degradation states, few of which can directly and effectively reflect the current state and analyze condition monitoring data, maintenance measures, and reliability together at the same time. In this paper, we introduce the performance margin as a decision criterion of CBM. We propose a condition-based maintenance optimization method using performance margin. Considering a CBM optimization problem for a degrading and periodically inspected component, a newly developed performance margin degradation model is established when three different maintenance measures become involved. Maintenance measure effect factors, maintenance decision vectors, and maintenance measure threshold vectors are developed to update the degradation model. And to build a maintenance optimization model, both cost and loss related to maintenance decision problems and reliability obtained by performance margin have been taken into consideration. Finally, a numerical example is provided to illustrate the proposed optimization method.