Artificial intelligence for fault diagnosis of rotating machinery: A review

Liu, Ruonan; Yang, Boyuan; Zio, Enrico; Chen, Xuefeng

doi:10.1016/j.ymssp.2018.02.016

Cited by 1,613 publications

(856 citation statements)

References 95 publications

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“…In some contexts, such as rotating machinery, which is among the most important equipments in modern industry, fault diagnosis can be regarded as a pattern recognition problem. Due to the variability and richness of the response signals relevant to the rotating machinery condition, it is almost impossible to recognize fault patterns directly: Artificial Intelligence techniques, encompassing a preprocessing step for feature extraction and an online step for fault recognition, are very promising . Specifically, several methods have been used for performing the latter step, including convex optimization, mathematical optimization, as well as classification, statistical learning, and probability‐based algorithms.…”

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

“…Due to the variability and richness of the response signals relevant to the rotating machinery condition, it is almost impossible to recognize fault patterns directly: Artificial Intelligence techniques, encompassing a preprocessing step for feature extraction and an online step for fault recognition, are very promising. 9 Specifically, several methods have been used for performing the latter step, including convex optimization, mathematical optimization, as well as classification, statistical learning, and probability-based algorithms. An advantage of model-based methods, with respect to data-driven methods, is that fault detection and isolation can be achieved without the need for data from different faults.…”

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

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Twin‐engined diagnosis of discrete‐event systems

Bertoglio

Lamperti

Zanella

et al. 2019

Engineering Reports

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Diagnosis of discrete‐event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby making the approach impractical even for DESs of moderate size. To avoid total knowledge compilation while preserving efficiency, a twin‐engined diagnosis technique is proposed in this paper, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the symptom dictionary of the DES. This way, if the same diagnosis problem arises anew, then it will be solved by Engine 1 in linear time. The symptom dictionary can also be extended by specialized knowledge coming from scenarios, which are the most critical/probable behavioral patterns of the DES, which need to be diagnosed quickly.

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Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

Section: Diagnosis and The Myth Of Total Knowledge Compilationmentioning

confidence: 99%

Twin‐engined diagnosis of discrete‐event systems

Bertoglio

Lamperti

Zanella

et al. 2019

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…Therefore, there may exist many unique features or patterns hidden in the data themselves that can potentially reveal a bearing fault, and it is almost impossible for humans to identify these convoluted features through manual observation or interpretation. Therefore, many researchers have applied various machine learning (ML) algorithms, including artificial neural networks (ANN), principal component analysis (PCA), support vector machines (SVM), etc., to parse the data, learn from them, and apply what they've learned to make intelligent decisions regarding the presence of bearing faults [18]- [21]. Most of the literature applying these ML algorithms report satisfactory results with classification accuracy over 90%.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

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“…Meanwhile, the vibration signal of bearings often contains abundant noise signals due to the complex working conditions, which adds a great deal of difficulties to the failure form and performance prediction of rolling bearings, affecting the accuracy of judgment. Hence, noise reduction is quite vital and meaningful before the diagnosis [1].…”

Section: Introductionmentioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

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Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

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Artificial intelligence for fault diagnosis of rotating machinery: A review

Cited by 1,613 publications

References 95 publications

Twin‐engined diagnosis of discrete‐event systems

Twin‐engined diagnosis of discrete‐event systems

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

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