Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

Li, Xiang; Zhang, Wei; Xu, Nan-Xi; Ding, Qian

doi:10.1109/tie.2019.2935987

Cited by 151 publications

(48 citation statements)

References 28 publications

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“…Although the implementation of Gaussian components in our proposed model enables separately modeling different stochastic events and separately modeling scenarios governed by different rules [33], it is unable to produce an accurate prediction for new coming data that have large variations from the learning datasets (i.e., data follow a new degradation mode that has not been observed before). To tackle this problem, concepts such as physicsinformed [34] or domain adaption [35] machine learning models are encouraged.…”

Section: Extended Discussionmentioning

confidence: 99%

Prognostics with Variational Auto-encoder by Generative Adversarial Learning

Tang¹,

Huang²

2020

Preprint

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<div>Prognostics predicts future performance progression</div><div>and remaining useful life (RUL) of in-service systems</div><div>based on historical/contemporary data. One of the challenges for prognostics is the development of methods which are capable of handling real-world uncertainties that typically lead to inaccurate predictions. To alleviate the impacts of uncertainties and to achieve accurate degradation trajectory and RUL predictions, a novel sequence-to-sequence predictive model is proposed based on a variational auto-encoder (VAE) that is trained with Generative Adversarial Networks (GAN). A Long Short-Term Memory (LSTM) network and Gaussian mixture model are utilized as building blocks so that the model is capable of providing probabilistic predictions. Correlative and monotonic metrics are applied to identify sensitive features in the degradation progress, in order to reduce the uncertainty induced by raw data. Then, the extracted features are concatenated with one-hot health</div><div>state indicators as training data for the model to learn the end-of-life (EoL) without the need for prior knowledge of failure thresholds. The performance of the proposed model is validated by health monitoring data collected from real-world aero-engines, wind turbines, and lithium-ion batteries. The results demonstrate that significant performance improvement can be achieved in long-term degradation progress and RUL prediction tasks.</div>

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Section: Extended Discussionmentioning

confidence: 99%

Prognostics with Variational Auto-encoder by Generative Adversarial Learning

Tang¹,

Huang²

2020

Preprint

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“…In [144], a domain adaptation method for MFDD based on deep learning in which adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments concerning different machine health conditions. Here, FFT is firstly applied to the temporal signals to obtain the frequencydomain information, which is then fed into the network as inputs.…”

Section: Deep Transfer Learning and Domain Adaptation Methodsmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

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“…where fitness-the accuracy of the USELM classification of parameters by NN and lam; fnew-the fitness at this time (the purpose is to find the NN and lam under the minimum fnew). the new fitness value and the fitness value obtained in step (4). Compare and get the best nest; (6) Find the best nest from step (5).…”

Section: B Optimized Uselm (Ouselm) For Fault Isolationmentioning

confidence: 99%

“…production and intelligent manufacturing [1]- [3]. At the same time, the health maintenance and management of mechanical system has attracted more and more attention of the enterprises and research staff [4], [5]. That is to say, an effective and appropriate mechanical equipment condition monitoring and fault diagnosis framework can not only ensure the safe operation of rotating machinery, but also can reduce unnecessary faults, increase the service life of mechanical equipment, and improve the economic benefits of the entire industrial system [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder With Optimized Unsupervised Extreme Learning Machine

2020

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Nowadays, the intelligent fault diagnosis based on deep learning have achieved remarkable results in the fields of the industrial equipment health monitoring and management. To implement the adaptive feature extraction and fault isolation for key components of rotating machinery (rolling bearings, etc.), two new algorithms, Adaptive Sparse Contrative Auto-encoder (ASCAE) algorithm and Optimized Unsupervised Extreme Learning Machine (OUSELM) classifier by Cuckoo Search Algorithm (CSA), can be firstly designed in this paper, respectively. Furthermore, a new rolling bearing fault diagnosis framework based on ASCAE combined with OUSELM is first of all proposed in this paper. Accordingly, this designed fault diagnosis framework can be divided into three main steps: i). Firstly, the vibration signals of rolling bearings can be collected and processed on the key components of rotating machinery, and then the collected vibration signals can be accordingly converted into frequency signals; ii). Secondly, the transformed spectral signals can be entered into the constructed ASCAE for feature learning to exploit the multi-layer sensitive features from the hidden raw data; iii). Thirdly, the extracted multi-layer sensitive features can be flowed into the trained OUSELM classifier for unsupervised fault state separation and diagnosis. More specifically, our designed fault diagnosis framework (ASCAE-OUSELM) can employ the homotopy regularization theory, sparse theory, intelligent optimization algorithm and other tools to optimize the parameters and improve the performance of the original Contrative Auto-encoder (CAE) algorithm and Unsupervised Extreme Learning Machine (USLEM) algorithm, respectively. At the same time, the proposed fault diagnosis framework can achieve effective sparse and sensitive feature information extraction in the feature extraction stage (ASCAE) to avoid over-fitting. In fault isolation stage, the issue of the supervised and low training efficiency caused by traditional deep learning model can be perfectly addressed by OUSELM. Eventually, the experimental data of rolling bearings validated the effectiveness of the proposed fault diagnosis framework and two deigned algorithms. INDEX TERMS Rolling bearings, fault diagnosis framework, homotopy regularization, Adaptive Sparse Contrative Auto-encoder (ASCAE), Optimized Unsupervised extreme learning machine (OUSELM), Cuckoo Search Algorithm (CSA).

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Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

Cited by 151 publications

References 28 publications

Prognostics with Variational Auto-encoder by Generative Adversarial Learning

Prognostics with Variational Auto-encoder by Generative Adversarial Learning

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder With Optimized Unsupervised Extreme Learning Machine

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