Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests

Li, Huifang; Hu, Guangzheng; Li, Jianqiang; Zhou, MengChu

doi:10.1109/tase.2020.3048056

Cited by 64 publications

(21 citation statements)

References 44 publications

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“…To verify the effectiveness of the method in this paper, two classic methods (SVM [ 41 ] and DBN [ 42 ]) and three latest methods (SPBO-SDAE [ 11 ], PSO-DNN [ 12 ] and CS-IMSNs [ 13 ]) were selected for comparison. The brief settings of these fault diagnosis methods are listed in Table 5 .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Shan

Bao

et al. 2022

Sensors

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With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling and fast fault diagnosis. The data sampling module non-linearly projects the intensive raw monitoring data into low-dimensional sampling space, which effectively reduces the pressure of transmission, storage and calculation. The fast fault diagnosis module introduces the kernel function into DELM to accommodate sparse signals and then digs into the inner connection between the compressed sampled signal and the fault types to achieve fast fault diagnosis. This work takes full advantage of the sparsity of the signal to enable fast fault diagnosis online. It is a general method in industrial embedded systems under data-driven conditions. The results on the CWRU dataset and real platforms show that our method not only has a significant speed advantage but also maintains a high accuracy, which verifies the practical application value in industrial embedded systems.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Fault classification refers to predicting the fault category of unknown signals by learning data features. The research on fault classification optimization is relatively mature and includes the latest algorithms: ANN [ 10 ], SPBO-SDAE [ 11 ], PSO-DNN [ 12 ] and CS-IMSNs [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Shan

Bao

et al. 2022

Sensors

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“…(5.) MHMM [11] Modified HMM for TWM --Online MoG-HMM [12] Learns RUL distributions with MoG-HMM --HMM and NF [13] Predicts degradation with Neuro-Fuzzy ----HSMM [14] Uses HSMM to predict RUL ---HMM for TWM [15] Uses HMM to predict RUL ---HMM ensemble [16] HMM ensemble to predict RUL --Neo Fuzzy [17] Predicts RMS with Neuro-Fuzzy -----FDFDA [18] FD analysis and regression updating ---ANN for TWM [19] Convolutional ANN for wear classification -----RNN with HI [20] RNN for HI and RUL estimation ----Fault effects [21] Uses fault effects to predict RUL ---LSTM-SVM [22] LSTM-SVM for RUL prediction ---LSTM with PF [23] Uses LSTM networks and PF to predict RUL ---BDNN-RF [24] BDNN and RF for ball-bearing fault prediction ----Regression [25] Regression models for RUL estimation --EKM for TWM [26] EKF and regression to predict RUL --WPD-HMM [27] Log-likelihood of HMM as HI --AHMM [28] Adaptive HMM for TWM -Trigger regression [29] Triggered regression for RUL estimation -APCMD [30] Local and global regressions to predict RUL -HSIC [31] Changes in dependencies as degradation --Random Forest [32] Uses random forest to detect anomalies ---Genetic HMMs [33] Learns HMMs with genetic algorithms --AMBi-GAN [34] Uses GAN to detect anomalies --…”

Section: Papermentioning

confidence: 99%

Asymmetric HMMs for Online Ball-Bearing Health Assessments

Puerto-Santana¹,

Bielza

Díaz-Rozo³

et al. 2022

IEEE Internet Things J.

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The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learningbased monitoring systems can estimate the remaining useful life (RUL) of ball-bearings, reducing the downtime by early failure detection. However, traditional approaches for predictive systems require run-to-failure (RTF) data as training data, which in real scenarios can be scarce and expensive to obtain as the expected useful life could be measured in years. Therefore, to overcome the need of RTF, we propose a new methodology based on online novelty detection and asymmetrical hidden Markov models (As-HMM) to work out the health assessment. This new methodology does not require previous RTF data and can adapt to natural degradation of mechanical components over time in data-stream and online environments. As the system is designed to work online within the electrical cabinet of machines it has to be deployed using embedded electronics. Therefore, a performance analysis of As-HMM is presented to detect the strengths and critical points of the algorithm. To validate our approach, we use real life ball-bearing data-sets and compare our methodology with other methodologies where no RTF data is needed and check the advantages in RUL prediction and health monitoring. As a result, we showcase a complete end-to-end solution from the sensor to actionable insights regarding RUL estimation towards maintenance application in real industrial environments.

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“…Harutyunyan et al [10] developed a fault detection method based on multi-level model by using the hierarchical structure of detection and diagnosis methods. Li et al [11] proposed a new fault diagnosis model which combined binarized deep neural network with improved random forests for real-time fault diagnosis. Lu et al [12] presented an innovative diagnosis model using the complementary ensemble empirical mode decomposition with kernel support vector machines to evaluate the health condition of bearings in terms of defect severity.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis

Wang¹,

Zhao²,

Cao³

2022

J. vibroeng.

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The extraction of early fault features from time-series data is very crucial for convolutional neural networks (CNNs) in bearing fault diagnosis. To address this problem, a CNN framework based on identity mapping and Adam optimizer is presented for learning temporal dependencies and extracting fault features. The introduction of four identity mappings allows the deep layers to directly learn the data from the shallow layers, which alleviates the gradient disappearance problem caused by the increase of network depth. A new Adam optimizer with power-exponential learning rate is proposed to control the iteration direction and step size of CNN method, which solves the problems of local minima, overshoot or oscillation caused by the fixed values of the learning rates during the updating of network parameters. Compared to existed methods, the identification accuracy of the proposed method outperformed that of other methods for bearing fault diagnosis.

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Intelligent Fault Diagnosis for Large-Scale Rotating Machines Using Binarized Deep Neural Networks and Random Forests

Cited by 64 publications

References 44 publications

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Asymmetric HMMs for Online Ball-Bearing Health Assessments

A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis

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