Fault Diagnosis Based on A Stacked Sparse Auto-Encoder Network and KNN Classifier

Yan, Zichen; Yuan, Xianfeng; Zhou, Fengyu; Song, Yong; Xu, Qingyang; Shao, Yang

doi:10.1109/cac48633.2019.8996595

Cited by 4 publications

(10 citation statements)

References 12 publications

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Section: K-nearest Neighbours (K-nn)mentioning

confidence: 99%

“…Yan et al [54] presented a hybrid intelligent fault diagnostic model for rolling bearings that combines a k-NN classifier with a stacked sparse auto-encoding network (SSAE). Their model used the advantage of the k-NN algorithm that it can deal with multi-classification problems and improve the accuracy of their model [54]. Overall, in comparison to traditional methods using deep neural networks for feature extraction avoids being overly dependent on professional knowledge and improves the accuracy of the fault classification [54].…”

Section: K-nearest Neighbours (K-nn)mentioning

confidence: 99%

See 1 more Smart Citation

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

Althubaiti¹,

Elasha²,

Teixeira³

2021

J. vibroeng.

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There is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. This can be achieved through improving diagnosis and prognosis of bearing faults to better determine bearing remaining useful life (RUL). Until now there has been limited research into the prognosis of bearing life in rotating machines. Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new prognosis techniques. Different approaches make use of the advantages of each technique while overcoming the disadvantages towards the development of intelligent systems to determine the RUL of bearings. The review shows that while there are numerous approaches to diagnosis and prognosis, they are suitable for certain cases or are domain specific and cannot be generalised.

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Section: K-nearest Neighbours (K-nn)mentioning

confidence: 99%

Section: K-nearest Neighbours (K-nn)mentioning

confidence: 99%

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

Althubaiti¹,

Elasha²,

Teixeira³

2021

J. vibroeng.

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“…It shows advantages such as fast speed of search and satisfactory optimization effect [23]. However, there is still room for improvement in terms of the search strategy for the GWO [24,25]. erefore, an improvement is made to the proposed α Grey Wolf Optimization (α-GWO) algorithm as follows.…”

Section: An Improved Grey Wolf Algorithmmentioning

confidence: 99%

Sensor Fault Diagnosis Method Based on α‐Grey Wolf Optimization‐Support Vector Machine

Cheng

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of α Grey Wolf Optimization Support Vector Machine (α-GWO-SVM) in this paper. Firstly, a fusion with Kernel Principal Component Analysis (KPCA) and time-domain parameters is performed to carry out the feature extraction and dimensionality reduction for fault data. Then, an improved Grey Wolf Optimization (GWO) algorithm is applied to enhance its global search capability while speeding up the convergence, for the purpose of further optimizing the parameters of SVM. Finally, the experimental results are obtained to suggest that the proposed method performs better in optimization than the other intelligent diagnosis algorithms based on SVM, which improves the accuracy of fault diagnosis effectively.

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“…If incipient fault diagnosis can be achieved and appropriate predictive maintenance measures can be taken in time, planned shutdowns and replacement of parts for high-end equipment can be arranged well before a failure occurs. This has important theoretical significance and engineering value for realizing the safe and stable operation of mechanical equipment [2].…”

Section: Introductionmentioning

confidence: 99%

“…Based on this, they proposed a combination of kmeans clustering and k-nearest neighbor for fault diagnosis. Yan et al [2] proposed a hybrid intelligent fault diagnosis model combining a hierarchical sparse self-encoding network and a KNN classifier. Cheng et al [14] analyzed and diagnosed rolling bearing faults by extracting four information entropies in the time domain, frequency domain, and time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features

Wang

Wei

et al. 2021

IEEE Access

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Research on the intelligent fault diagnosis method of rolling bearing based on laboratory data has made some achievements. However, due to the change of working conditions and the lack of historical data of the same equipment in the actual diagnosis, some methods mostly have problems such as poor generalization. Model training and verification data are insufficient, and engineering practice still lacks effective intelligent fault diagnosis methods. In this paper, we propose a weighted k-nearest neighbor (WKNN) fault diagnosis model based on multi-dimensional sensitive features, and propose a fault diagnosis method for rolling bearings that adapts to different equipment and different operating conditions. First, we extract time domain, frequency domain, and entropy features of the original signal to form the raw signal feature set. Then, the iterative ReliefF feature screening method is used to evaluate the joint feature set, calculate the weight of each feature, remove insensitive and redundant features, and obtain a highdimensional sensitive feature set. Finally, the WKNN classification model is used to identify bearing failure modes. The fault diagnosis model was trained using rolling bearing data from the Case Western Reserve University (CWRU), while laboratory data from the Intelligent Maintenance System (IMS), the Society of Mechanical Failure Prevention Technology (MFPT) and the engineering case data were used for testing. The results show that the model proposed in this paper has high fault diagnosis accuracy and can accurately determine the fault type after early warning. Compared with other comparison methods, the fault recognition accuracy rate is higher. And it is suitable for different working conditions and different equipment, and has good engineering application value.INDEX TERMS Different working conditions, fault diagnosis, multi-dimensional sensitive features, ReliefF, WKNN.

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Fault Diagnosis Based on A Stacked Sparse Auto-Encoder Network and KNN Classifier

Cited by 4 publications

References 12 publications

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

Sensor Fault Diagnosis Method Based on α‐Grey Wolf Optimization‐Support Vector Machine

Weighted K-NN Classification Method of Bearings Fault Diagnosis With Multi-Dimensional Sensitive Features

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