Real-Time Diagnosis of Abrupt and Incipient Faults in IMU Using a Lightweight CNN-Transformer Hybrid Model

A survey and classification of incipient fault diagnosis approaches

In order to ensure the safe operation of industrial systems, the timely diagnosis of incipient faults is gradually gaining attention. The kernel entropy component analysis (KECA) has been widely used in the fault diagnosis of nonlinear industrial processes. However, the KECA often performs unsatisfactorily in the case of incipient faults. Therefore, a novel incipient fault detection and diagnosis method based on the statistical feature KECA integrating the twofold weighted (TWSFKECA) is proposed. The residual function in the local approach is combined with the KECA to construct statistical features of the data. Then, in order to highlight the influence of incipient faults of statistical features, the statistical feature sample weighting strategy is established based on the dissimilarity analysis between the test and training samples. Furthermore, the statistical feature component weighting strategy is developed for the sensitive components, which are judged by applying the Durbin-Watson (DW) criterion to calculate the extent to which the sample-weighted statistical feature components contain significant information. Moreover, based on the statistical features of twofold weights, two statistics indexes are created for incipient fault detection. In addition, the strategy for process fault diagnosis using a variable contribution plot method is proposed to isolate faulty variables. Finally, the continuous stirred tank reactor control system and the Tennessee Eastman process illustrate the superiority of the proposed method for incipient fault detection and diagnosis. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Effective detection of incipient faults prevents the evolution of accidents and ensures the smooth operation of the production process. In nonlinear industrial processes, the statistical feature KECA integrating the twofold weighted is proposed for incipient fault detection and diagnosis. The residual function is introduced in the kernel entropy component analysis to construct the statistical features of the data, which helps extract the incipient fault information. Then, the twofold weighting strategy weights the statistical features in terms of samples and components, highlighting the influence of the main samples and sensitive components in the incipient faults, respectively. In addition, a variable contribution plot method is developed to solve the problem of not being able to find out the cause of faults through control plots. The experimental results further verify the applicability of the proposed method for monitoring the occurrence of incipient faults.

Relation between fault characteristic frequencies and local interpretability shapley additive explanations for continuous machine health monitoring

Incipient faults in high-speed trains are usually masked by noises and disturbances from process and sensors, which severely increases the difficulty of incipient fault detection and diagnosis. By introducing Hellinger distance into multivariate statistical analysis framework, this paper develops a robust detection and diagnosis method for incipient faults under the principal component analysis. The proposed method can detect all incipient sensor faults in traction systems of high-speed trains in real time by comparing reference probability density functions (PDFs) with the online estimated PDFs. According to the fault detection information, an accurate fault diagnosis can be achieved online through Bayesian inference. Key advantages of the proposed method are its salient robustness to unknown noises and disturbances, as well as the high sensitivity to incipient faults. In addition, the proposed method does not require any information on system models of high-speed trains or any human intervention. The effectiveness of the proposed method has been firstly proven by mathematical derivations and then been verified by numerical simulations. Finally, the proposed method has been applied to the practical experiment platform of the high-speed trains.

The importance of induction motor (IM) drives in industrial applications, especially in the renewable energy sector is unarguable due to their fast-dynamic response. Their monitoring is key to avoid downtime and economic losses. Even though a lot of research has been focused on incipient fault diagnosis in induction machines, early diagnosis in power electronic drive-fed machines is still a challenge. In this article, a novel two-level hybrid hierarchical convolution neural network with support vector machine (HCNN-SVM) is proposed for incipient interturn fault diagnosis of drive-fed machines. The first level of the proposed structure is intended to recognize the pattern of interturn fault in drive-fed IM, while the second level is developed to identify the fault severity. First, the effective features are extracted automatically using the shared layers of HCNN for fault diagnosis and fault severity evaluation at the same time. The features obtained using the HCNN are suitably used to train SVM for classification. Comparison of the results with the existing architectures, namely, HCNN and SVM, show the effectiveness of proposed hybrid method. The experimental results show that the hybrid HCNN-SVM is fast and highly accurate in identifying the interturn fault pattern and evaluating its severity.

The growing attention paid to industrial condition-based maintenance during the last decade has increased the interest in incipient fault detection and diagnosis of complex systems. For these types of faults leading to slight changes in an early stage, the detection and isolation are subtle and can easily be covered by noise. It is then a challenging problem in fault diagnosis to obtain sufficiently sensitive, accurate, and robust techniques. This paper proposes a sensitive fault diagnosis method referred to as Local Mahalanobis Distance Analysis (LMDA) for incipient fault detection in multivariate nonlinear systems. In this method, we define the local Mahalanobis distance to recognize outliers in an unknown distribution. To speed up its computation, which depends on the sample size, we propose a distance-based down-sampling algorithm that can remove redundancy information from samples and hence reduce sample size. Based on this operation, a local Mahalanobis distance signal can be computed with acceptable time consumption for online monitoring. Considering the difficulty of detecting incipient faults, we finally use the LMD envelope as the discriminate value in the detection procedure to improve the detection sensitivity. A case study using the Continuous-flow Stirred Tank Reactor (CSTR) is proposed to check and validate the proposed methodology's effectiveness. The performances evaluated in terms of detection delay, false alarm rate, missed detection rate, and area under the receiver operating characteristic curve (AUC) show that our proposal outperforms state-of-the-art-based solutions in sensitivity, accuracy, and robustness.

Diagnosis of incipient fault is critical for safe operation of the system because it can prevent disastrous accidents from happening by diagnosing the early fault before deterioration. Deep learning is efficient in feature extraction but it requires a large number of samples to train traditional deep neural network (DNN). It is thus inevitable that the efficiency of DNN will be affected when it is applied to incipient fault diagnosis for there are usually a very limited number of incipient fault samples. Furthermore, a large amount of information involved in significant fault samples was not adequately used for incipient fault diagnosis. To solve this problem, this paper proposes an incipient fault diagnosis model with DNN-based transfer learning. The model can extract fault feature involved in a large number of significant fault samples and apply it to extract insignificant fault feature with a small number of incipient fault samples. In this way, the proposed transfer learning method can efficiently diagnose incipient fault in the case when only a limited number of incipient fault data is available. The efficiency of the proposed model is demonstrated by utilizing the Case Western Reserve University bearing data set.

This study addresses the problem of incipient fault detection and diagnosis for Takagi–Sugeno (T–S) fuzzy systems and explores further results of total measurable fault information residual (ToMFIR). First, T–S fuzzy model is used to describe the global dynamics of a non‐linear system and the model of incipient actuator faults is formalised. Second, based on the ToMFIR, a novel incipient fault detection method is proposed, which removes the assumptions on system structure in some existing work. Further, sliding‐mode observers combined with ToMFIR‐based thresholds are designed for incipient fault isolation. Finally, application results conducted on a high‐speed railway traction device are given to illustrate the effectiveness of the proposed approach.

Data-driven and deep learning-based detection and diagnosis of incipient faults with application to electrical traction systems

Induction machines are an integral part of any major industry or production process. Incipient fault diagnosis is an important topic which aims at detecting the fault at an early stage and isolating them from other ambiguous conditions. In this work, an analytical model for inter-turn fault diagnosis in induction machines has been developed. A methodology for early diagnosis of fault has been envisaged, in presence of ambiguous conditions such as voltage imbalances and load variations. The novel method is based on motor current signature analysis (MCSA), using deep learning based one dimensional convolutional neural network(1D-CNN) model and long short term model(LSTM). The results using these two methods have been compared, and this initial investigation shows that CNN is found to be more suitable than LSTM, for incipient fault diagnosis.

Category knowledge-guided few-shot bearing fault diagnosis

A key-factor denoising strategy for quasi periodic non-stationary incipient faults diagnosis

The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H2, C2H4/C2H6, and C2H2/CH4 as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods, i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research.

Incipient fault diagnosis using support vector machines based on monitoring continuous decision functions

The diagnosis of incipient fault is important for power transformer condition monitoring. The incipient faults are monitored by conventional and artificial intelligence based models. The key gases, percentage value of gases and ratio of Doernenburg, Roger, IEC methods are input variables to artificial intelligence (AI) models which affects the accuracy of incipient fault diagnosis so selection of most influencing relevant input variable is an important research area. With this main objective, RapidMiner software is applied to IEC TC 10 and related datasets having different operating life to find most influencing input variables for incipient fault diagnosis in AI models. The RapidMiner identifies %CH 4 , %C 2 H 2 , %H 2 , %C 2 H 6 , C 2 H 4 /C 2 H 6 , C 2 H 2 /CH 4 , C 2 H 2 /H 2 and CH 4 /H 2 as the most relevant input variables in incipient fault diagnosis and it is used for fault diagnosis using different artificial intelligence (AI) approach i.e. fuzzy-logic (FL) and . The compared results shows that AI models give better results at proposed input variables used as an input vector. PNN gives highest accuracy of 98.28, proving proposed input variables can be used in transformer fault diagnosis research.