Condition Monitoring And Fault Diagnosis Research Articles

Wind turbine generator early fault diagnosis using LSTM-based stacked denoising autoencoder network and stacking algorithm

ABSTRACT To reduce the significant economic losses caused by the fault deterioration of wind turbine generators, it is urgent to detect and diagnose the early faults of generators. The existing condition monitoring and fault diagnosis (CMFD) methods have disadvantages of less considering data temporal characteristic, acquiring early faults with difficulty, and having lower diagnostic accuracy. To address those limitations, a novel LSDAE-stacking CMFD method of generators was proposed. Specifically, a multivariate spatiotemporal condition monitoring model (LSDAE) was established by combining the LSTM and SDAE networks, which can detect generator early anomalies through real-time monitoring the reconstruction residual. Then, based on the stacking ensemble algorithm, a multi-classification fault diagnosis model (Stacking) was constructed to identify early fault types, which can integrate advantages of different base-classifiers to achieve a better diagnostic accuracy. Case studies on three actual generator failures were employed to validate the effectiveness and accuracy of the proposed LSDAE-stacking method. The results illustrated that, compared with conventional SDAE model, the proposed LSDAE model had higher reconstruction precision and superior early-fault-warning capacities. And compared with traditional algorithms such as SVM, RF, AdaBoost, GBDT and XGBoost, the constructed Stacking model can effectively identify the fault types of generators and had higher diagnostic accuracy.

A hierarchical modeling strategy for condition monitoring and fault diagnosis of wind turbine using SCADA data

The development and utilization of wind energy can help promote the carbon neutrality. Condition monitoring and fault diagnosis based on supervisory control and data acquisition (SCADA) can effectively improve wind turbine reliability and reduce operation and maintenance (O&M) costs. However, the complex data characteristics (e.g., time-varying and distribution-free) make it difficult to use a unified model to comprehensively evaluate the turbine state. To finely detect and analyze the turbine faulty mode, a hierarchical condition monitoring and fault diagnosis (CMFD) method is proposed, which consists of variable-level and turbine-level. First, the SCADA data is divided into two blocks: the nonstationary part with time-varying distribution, and the stationary part with time-invariant but non-Gaussian distribution. At the variable-level, this paper proposed a local monitoring unit based on sparse cointegration analysis (SCA) and independent component analysis (ICA) to detect faulty samples of different variable blocks. And the fault-related variables are isolated based on the proposed distributed reconstruction. At the turbine-level, the evaluation results from variable-level are fused: on the one hand, Bayesian inference is used to integrate monitoring statistics of stationary and nonstationary parts to comprehensively evaluate the operational state of the turbine; on the other hand, the root fault subsystem is deduced from the fault-related variables isolated from the two parts based on Gaussian process regression. The proposed method is applied in the operation and maintenance of a real wind farm, and the performance advantages are verified by the comparison with popular benchmarks.

Wind turbine blades fault detection using system identification-based transmissibility analysis

Wind turbines (WTs) are extensively installed nowadays and the blades are integral components within the WT systems. Condition monitoring and fault diagnosis (CMFD) for WT blades is challenging due to the fact that they usually suffer from non-stationary time-varying loads and the load information is often unknown or hard to collect. This paper proposes system identification-based transmissibility function (TF) methods to effectively detect the blade defects and further help to prevent potential economic loss. The novelty is that the proposed methods only use output response information in the time domain, which can therefore remove the impact of the input excitation. Four different models are used in this work to estimate the blade structure system parameters, including the autoregressive with eXogenous input (ARX) model, the autoregressive moving average with eXogenous input (ARMAX) model, the output error (OE) model and the non-linear ARX polynomial model. Regularisation is then employed to address the overfitting issues that may occur during parameter estimation. The effectiveness of the proposed methods are demonstrated in the laboratory using three naturally damaged industrial-scale WT blades.

Review of condition monitoring and fault diagnosis for marine power systems

AbstractThe use of condition monitoring and fault diagnosis (CMFD) in marine power systems significantly influences ship safety. This study divides the development of CMFD for marine power systems into three periods and reviews the content, state and limitations of CMFD research for each period. According to the research achievements and engineering experience of the authors’ team, typical application cases are introduced for CMFD in ships, including CMFD platforms on engineering ships, salvage ships, container ships and ro-ro ships powered by solar photovoltaic systems. Finally, prospective research directions are proposed for CMFD in marine power systems, considering the research status of CMFD and the trend toward intelligent and eco-friendly ships.

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

Wind turbine blade bearings are often operated in harsh circumstances, which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 r/min). Over the past few years, acoustic emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for this study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation-based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

Doppler Distortion Removal in Wayside Circular Microphone Array Signals

Condition monitoring and fault diagnosis (CMFD) of key moving sources has attracted more and more attentions in recent years. Among many CMFD strategies, the wayside acoustic signal-based method can detect slight failure efficiently in time with a low-cost system. However, the nonlinear time-varying distance between the moving source and the microphone array would bring in serious Doppler distortion in the acquired signals. The Doppler distortion would result in serious amplitude distortion, frequency shift, and band extension, which is an obstacle for accurate CMFD. Considering the superior time-spatial characteristics of circular microphone array, this paper presents a short-time sparse singular value decomposition (SSVD) method based on wayside circular microphone array signals to eliminate the existed Doppler distortion. The procedure of the presented method is summarized as follows. First, the Doppler distortion array signals are decomposed into a series of array segments with a sliding window. Afterward, the corresponding moving angles (MAs) of each array segments are calculated with the introduced SSVD method. Finally, the fitting time-varying MA curve and time-domain interpolation resampling are employed to eliminate the Doppler distortion. Simulation results validated that the proposed method could achieve better localization accuracy under heavy background noise as compared to other traditional methods. Moreover, the effectiveness of the proposed method has been further verified with real experiments, which indicates excellent potential application prospects for moving vehicles, trains, planes, and so on.

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Editorial - Fault Diagnosis for Marine Propulsion System

The purpose of the special issue is to aggregate papers on new research findings and developments in Condition Monitoring and Fault Diagnosis for Marine Propulsion Systems. Marine propulsion system is the heart of a vessel. Its normal operation ensures the security and efficiency of the trip. However, due to particular working conditions, the hull is prone to deflection and hence speeds up the degradation of the key components in the Propulsion system. A simple failure of the main diesel engine or a crack on the propeller blade may lead to malfunction of the propulsion system. Condition monitoring and fault diagnosis (CMFD) can yield an advance warning of impending failure in the system, thereby helping in maintenance and corrective

Condition based monitoring, diagnosis and maintenance on operating equipments of a hydraulic generator unit

According to performance characteristics of operating equipments in a hydraulic generator unit (HGU), the relative techniques on condition monitoring and fault diagnosis (CMFD) are introduced in this paper, especially the key technologies are emphasized, such as equipment monitoring, expert system (ES), intelligent diagnosis and condition based maintenance (CBM). Meanwhile, according to the instructor on CBM proposed by State electric power corporation, based on integrated mode, the main steps on implementation of CBM are discussed in this paper.

New Intelligent Condition Monitoring and Fault Diagnosis System for Diesel Engines Using Embedded System

Diesel engines are power source of various vehicles. Their complex structures make it difficult to detect pending faults in a timely manner. In fact, simultaneous existence of different faults is frequently found in diesel engines. To ensure the security of diesel engines, it is involved with two issues. One is the on-line condition monitoring, and the other is the intelligent fault diagnosis. However, the investigations have been addressed to one of the two problems extensively in previous work while very limited work has been done to develop an on-line intelligent condition monitoring and fault diagnosis (CMFD) system. For this reason, a new intelligent CMFD system is proposed to on-line analyze the vibration signals using the Hessian-based locally linear embedding (HLLE) and support vector machine (SVM) in this work to detect the diesel engine faults and to prevent the malfunction of the machines. The vibration signals were firstly recorded on-line by an embedded system. Then the HLLE was used to reduce the dimensions of original vibration signals to extract useful fault features, and lastly the SVM was adopted for faults detection. Experimental tests have been implemented to evaluate the performance of the on-line intelligent CMFD system. The diagnosis results demonstrate that the proposed method is very effective for the fault diagnosis of diesel engines. The faults can be detected on-line with a high rate of 90%

Condition Monitoring and Fault Diagnosis for Marine Diesel Engines using Information Fusion Techniques

The structural complexity of marine diesel engines and the failure transmission path significantly influence the quality of a measured vibration signal. A set of accelerometers have been involved in the condition monitoring and fault diagnosis (CMFD). To make full use of multi-channel sensor signals, a new information fusion method is proposed for the CMFD of marine diesel engines in this paper. For the signal fusion, the independent component analysis (ICA) was firstly adopted to separate useful source signal close to the engine vibration characteristics of the fault components from multi-channel sensors. Then the short time Fourier transform (STFT) was applied to the fault feature extraction and the principal component analysis (PCA) was used to fuse the feature space from a high dimension into a very low one. Followed, a Fuzzy neural network (FNN) classifier was employed to identify the engine faults. The real vibration data measured on a ship using four-channel sensors was used to evaluate the proposed method. The experimental diagnostic results demonstrate that the developed diagnostic method captures distinct time-frequency features of the vibration signals for monitoring the engine health condition with a fault detection rate of 90.5%. Moreover, the performance of the proposed method is superior to that without information fusion processing. Thus, the proposed method is feasible and available for the CMFD of marine diesel engines. Ill. 5, bibl. 11, tabl. 1 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.123.7.2387

Improving the performance of CMFD applications using multiple classifiers and a fusion framework

In this paper, we demonstrate that the performance of a condition monitoring and fault diagnosis (CMFD) system may be improved by combining the outputs from three ‘primary’ classifiers using a novel, hybrid, data-fusion approach. The resulting classifier system involves four key processing stages. In Stage One, three diverse primary classifiers are employed. In Stage Two, the outputs from the primary classifiers are combined using majority voting. In Stage Three, any unclassified patterns from Stage Two are reassessed using Dempster-Shafer theory. Finally, in Stage Four, a simple rule base (‘expert system’) is used to integrate the results from the earlier stages. We demonstrate the effectiveness of this framework on a data set intended to allow the detection of static thermostatic valve faults in a diesel engine cooling system. Overall performance of the classifier system is shown to improve from approximately 89% (using the best of the primary classifiers) to approximately 99% (using the framework). In addition, the misclassification level of the original primary classifiers is shown to be approximately 10%, while the equivalent rate for the fusion classifier is approximately 1%. We go on to describe the application of the same classifier framework to a very different problem domain (medical diagnostics) and obtain a similar improvement in system performance. Here, overall performance of the classifier system is again shown to improve from approximately 87% (using the best of the primary classifiers) to approximately 99% (using the framework). In this case, the misclassification level of the original primary classifiers is approximately 12%, while the equivalent rate for the fusion classifier is approximately 1%. The results suggest that this framework may be appropriate for use in a range of application areas.

Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems

In this paper, results are presented from a comprehensive series of studies aimed at assessing the suitability of multilayered perceptron (MLP) and radial basis function (RBF) networks for use in embedded, microcontroller-based, condition monitoring and fault diagnosis (CMFD) applications. Our assessment criteria include the performance of each classifier on a range of CMFD-related problems, such as situations where there may be multiple faults present simultaneously, or where ‘unknown’ faults may occur. In addition, the processor and memory requirements of each classifier are compared and discussed. On the basis of the results obtained in these studies, it is argued that each form of classifier has both strengths and weaknesses, and that neither is suitable for use in all CMFD applications. The paper concludes by demonstrating that, where memory and processor limits allow, the best performance may be obtained through use of a fusion classifier containing both MLP and RBF components.

A review of condition monitoring and fault diagnosis for diesel engines

AbstractTechnical advances and environmental legislation in recent years have stimulated the development of a number of techniques for condition monitoring and fault diagnosis (CMFD) in diesel engines. This paper firstly summarises common faults, fault mechanisms and their effect on diesel engine performance. Corresponding measurands are presented. Standard CMFD methods for parameters and CMFD systems for diesel engines are reviewed. Finally, some advanced CMFD techniques, including neural networks and fuzzy logic, which may be more powerful, are discussed.

Fault Tolerant-Reconfigurable Control for a Mass-Spring System

The paper presents a novel fault tolerant-reconfigurable control (FTRC) scheme based on condition monitoring and fault diagnosis (CMFD) for inherent dynamic change and anticipated faults of a mass-spring system. The strategy of the FTRC is that the four pre-designed controllers are switched according to the results of CMFD and reconfigurable logic. Simulations demonstrate that the control scheme can effectively and rapidly accommodate the inherent dynamic alternation and the break of spring in the plant, and the controlled system is stable and has a good tracking performance of position and velocity.

DISCUSSION ON SHIP MACHINERY MAINTENANCE BASED ON OIL MONITORING

It is important for shipping companies to reform their ship machinery maintenance system in order to increase profits. The ideal method, preventive maintenance, has been applied. For predictive maintenance, the techniques of condition monitoring and fault diagnosis (CM/FD) should be used. Due to voyages, there are some difficulties in designing the maintenance system for the ship machinery based on CM/FD. The principles, methods and programs of oil monitoring are discussed in this paper. From the monitored experiences of diesel engines mounted in tugboats, a suitable maintenance system is suggested by the authors.