FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Magar, Rishikesh; Ghule, Lalit; Li, Junhan; Zhao, Yang; Farimani, Amir Barati

doi:10.1109/access.2021.3056944

Cited by 55 publications

(31 citation statements)

References 34 publications

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“…Figures 5 and 6 demonstrate confusion matrix and ROC for set obtained through entropy of inner race fault for the classification of fault diameter in bearings [48], and Figure 7 shows the confusion matrix for set obtained from entropy of outer race fault for classification [49] of fault diameter in the bearings. It can be concluded from Table 2 that for any kind of wavelet, when fault diameter changes from 0.007 to 0.014, in case of inner race, the entropy increases in the range of 30-40%, while for fault [50] diameter change from 0.014 to 0.021, the entropy [51] was found to be decreased in the range of 40-50%.…”

Section: Resultsmentioning

confidence: 94%

Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network

Mishra¹,

Dubey²,

Swami

et al. 2022

Scientific Programming

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A running machine generates multi-frequency vibration signals which can be captured by accelerometers. Empirical mode decomposition, wavelet decomposition, and wavelet packet decomposition are the commonly used methods to decompose the multi-frequency signal. Quick fault classification, accurate signal decomposition, and fault size detection are still a problem in machines with rotary components. In the proposed work, fault diameter in rotary part of machine is detected and classified using the machine learning methods. In the first stage, we have employed empirical mode decomposition (EMD) for high-frequency noise removal. Residue signal is obtained by removing first IMF from base signal considering first IMF as a high-frequency noisy signal, followed by wavelet decomposition. Entropy of the wavelet coefficient obtained from 3rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. Three different sets have been taken for inner race, outer race, and ball race correspondingly. The proposed method classifies and detects the fault diameter up to 99.5%. The proposed method can be used for different types of continuous as well as discrete wavelets.

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Section: Resultsmentioning

confidence: 94%

Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network

Mishra¹,

Dubey²,

Swami

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…We For this dataset, the signals of 256,000 data points were clipped at the beginning and the ending by 3000 data points to avoid noise disturbance [24]. Then, 250,000 data points per signal were reshaped into the smaller signals of shape 50 × 50 2D arrays, which resulted in 100 smaller signals from each original signal.…”

Section: Resultsmentioning

confidence: 99%

Concise convolutional neural network model for fault detection

Firdausi

Ahmad

2022

CST

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Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.

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“…In [18] wavelet packet denoising and random forests are used to achieve 88.23% classification accuracy in fault diagnosis of rolling bearing and in noisy environment. In [19] a 2D CNN is proposed to improve classification accuracy of bearing faults by vibration data. However, such models usually require high computational complexity and memory footprint, so that they are not well suited for resource constrained embedded systems.…”

Section: Related Workmentioning

confidence: 99%

Low-Power Detection and Classification for In-Sensor Predictive Maintenance Based on Vibration Monitoring

et al. 2022

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In this work, a new custom design of an anomaly detection and classification system is proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform anomaly detection which cooperates with a mixed HW/SW Convolutional Neural Network (CNN) to perform the classification of detected anomalies. The AE features a partial binarization, so that the weights are binarized while the activations, associated to some selected layers, are non-binarized. This has been necessary to meet the severe area and energy constraints that allow it to be integrated on the same die as the MEMS sensors for which it serves as a neural accelerator. The CNN shares the feature extraction module with the AE, whereas a SW classifier is triggered by the AE when a fault is detected, working asynchronously to it. The AE has been mapped on a Xilinx Artix-7 FPGA, featuring an Output Data Rate (ODR) of 365 kHz and achieving a power dissipation of 333 W/MHz. Logic synthesis has targeted TSMC CMOS 65 nm, 90 nm, and 130 nm standard cells. Best results achieved highlight a power consumption of 138 μW/MHz with an area occupation of 0.49 mm 2 when real-time operations are set. These results enable the integration of the complete neural accelerator in the CMOS circuitry that typically sits with the inertial MEMS on the same silicon die. Comparisons with the related works suggest that the proposed system is capable of state-of-the-art performances and accuracy.

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FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

Cited by 55 publications

References 34 publications

Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network

Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network

Concise convolutional neural network model for fault detection

Low-Power Detection and Classification for In-Sensor Predictive Maintenance Based on Vibration Monitoring

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