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When rotating machinery fails, the consequent vibration signal contains rich fault feature information. However, the vibration signal bears the characteristics of nonlinearity and nonstationarity, and is easily disturbed by noise, thus it may be difficult to accurately extract hidden fault features. To extract effective fault features from the coll...
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In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving th...
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... Therefore, it holds great potential for application in the filed of diagnosing faults in rotating machinery. For example, Tang et al [78] utilized GA to determine the optimal embedding dimension and class number for composite dispersion entropy, as well as to optimize relevant parameters, resulting in significantly improved fault diagnosis efficiency. Tang et al [79] proposed a fault diagnosis method for double DBN bearings based on QGA and utilized quantum genetic algorithm (QGA) to optimize the parameters of Bi-DBN. ...
With the booming development of modern industrial technology, rotating machinery fault diagnosis is of great significance to improve the safety, efficiency and sustainable development of industrial production. Machine learning as an effective solution for fault identification, has advantages over traditional fault diagnosis solutions in processing complex data, achieving automation and intelligence, adapting to different fault types, and continuously optimizing. It has high application value and broad development prospects in the field of fault diagnosis of rotating machinery. Therefore, this article reviews machine learning and its applications in intelligent fault diagnosis technology and covers advanced topics in emerging deep learning techniques and optimization methods. Firstly, this article briefly introduces the theories of several main machine learning methods, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs) and related emerging deep learning technologies such as Transformer, adversarial neural network (GAN) and graph neural network (GNN) in recent years. The optimization techniques for diagnosing faults in rotating machinery are subsequently investigated. Then, a brief introduction is given to the papers on the application of these machine learning methods in the field of rotating machinery fault diagnosis, and the application characteristics of various methods are summarized. Finally, this survey discusses the problems to be solved by machine learning in fault diagnosis of rotating machinery and proposes an outlook.
... Entropybased measures such as approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. , have been widely employed in the field of fault diagnosis for rotating machinery. 22,23,24,25 Hamed Azami 26 proposed a complexity evaluation method for nonlinear time series based on dispersion entropy, which is robust against noise interference and computationally efficient. To further enhance the capability of extracting hidden fault features, Hamed Azami 27 subsequently introduced the multi-scale fluctuation dispersion entropy. ...
... However, it should be noted that inappropriate parameter settings in the calculation algorithm may lead to inaccurate extraction of hidden minor fault features. 25 Additionally, existing multi-scale entropy-based measures only analyze low-frequency components while neglecting high-frequency components, thereby overlooking potential fault feature information contained within different frequency segments. 28 In light of these limitations, this study conducts relevant research with the following main contributions: ...
... 27 It is calculated as follows. 25 The process begins by dividing the univariate signal L : a = {a 1 , a 2 , ..., a L } into non-overlapping segments of length τ , referred to as the scale factor. Let k denote the number of coarse-grained steps at this scale factor, constructing a coarse-grained time series: ...
Remaining useful life (RUL) prediction based on vibration signals is crucial for ensuring the safe operation and effective health management of rotating machinery. Existing studies often extract health indicators (HI) from time domain and frequency domain features to analyze complex vibration signals, but these features may not accurately capture the degradation process. In this study, we propose a degradation feature extraction method called Fusion of Multi-Modal Multi-Scale Entropy (FMME), which utilizes multi-modal Refined Composite Multi-scale Attention Entropy (RCMATE) and Fluctuation Dispersion Entropy (RCMFDE), to solve the problem that the existing degradation features cannot accurately reflect the degradation process. Firstly, the Empirical Mode Decomposition (EMD) is employed to decompose the dual-channel vibration signals of bearings into multiple modals. The main modals are then selected for further analysis. The subsequent step involves the extraction of RCMATE and RCMFDE from each modal, followed by wavelet denoising. Next, a novel metric is proposed to evaluate the quality of degradation features. The attention entropy and dispersion entropy of the optimal scales under different modals are fused using Laplacian Eigenmap (LE) to obtain the health indicators. Finally, RUL prediction is performed through the similarity of health indicators between fault samples and bearings to be predicted. Experimental results demonstrate that the proposed method yields favorable outcomes across diverse operating conditions.
... It requires regular monitoring of all system components, including electrical machines. Electrical machines operating increasingly under severe conditions or at the limits of their capacity are subject to faults that can lead to failures [1][2][3]. Investigations in several industrial fields have revealed that rolling bearing elements (RBEs) are the main sources of failures for almost 40% to 90% of low-to high-power machines [4,5]. Several studies [6,7] have concluded that bearing ball fault is the most difficult to diagnose (detection and classification) because its effects are strongly attenuated by the mechanical structure. ...
Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100% classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.
Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals.
Abstract With the aim to identify new fault diagnosis and advanced robotic systems, this paper first proposes a fault diagnosis algorithm based on an artificial immune network model that can adjust the pruning threshold. Secondly, the algorithm is improved based on neighbourhood rough set theory, in which the relationships among the pruning threshold, misdiagnosis rate, and missed diagnosis rate in the shape space are discussed. In addition, an improved algorithm for adjusting the adaptively pruning threshold based solely on an observation index is described. The simulation experiments show that the algorithm can identify the new fault modes while keeping the misdiagnosis and missed diagnosis rates low.
