scholarly journals Frequency Occurrence Plot-Based Convolutional Neural Network for Motor Fault Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1711
Author(s):  
Eduardo Jr Piedad ◽  
Yu-Tung Chen ◽  
Hong-Chan Chang ◽  
Cheng-Chien Kuo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Axis Deviation ◽  
Frequency Spectra ◽  
Load Variations ◽  
Vibration Sensors ◽  
New Feature ◽  
Frequency Occurrence

A novel motor fault diagnosis using only motor current signature is developed using a frequency occurrence plot-based convolutional neural network (FOP-CNN). In this study, a healthy motor and four identical motors with synthetically applied fault conditions—bearing axis deviation, stator coil inter-turn short circuiting, a broken rotor strip, and outer bearing ring damage—are tested. A set of 150 three-second sampling stator current signals from each motor fault condition are taken under five artificial coupling loads (0, 25%, 50%, 75% and 100%). The sampling signals are collected and processed into frequency occurrence plots (FOPs) which later serve as CNN inputs. This is done first by transforming the time series signals into its frequency spectra then convert these into two-dimensional FOPs. Fivefold stratified sampling cross-validation is performed. When motor load variations are considered as input labels, FOP-CNN predicts motor fault conditions with a 92.37% classification accuracy. It precisely classifies and recalls bearing axis deviation fault and healthy conditions with 99.92% and 96.13% f-scores, respectively. When motor loading variations are not used as input data labels, FOP-CNN still satisfactorily predicts motor condition with an 80.25% overall accuracy. FOP-CNN serves as a new feature extraction technique for time series input signals such as vibration sensors, thermocouples, and acoustics.

Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Decision Fusion ◽  
Fusion Model ◽  
Vibration Signals ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

Novel topology convolutional neural network fault diagnosis for aircraft actuators and their sensors

2021 ◽  
pp. 014233122110056
Author(s):  
Ruonan Wei ◽  
Ju Jiang ◽  
Haiyan Xu ◽  
Danmeng Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Working Conditions ◽  
Actuator Fault ◽  
Position Sensor ◽  
Residual Structure ◽  
Average Accuracy ◽  
Actuator System

The variable working conditions and frequent turns make the aircraft actuator system prone to failure, seriously threatening flight safety. The identification of the airplane actuator system is critical for flight decisions and safety. Most fault diagnosis methods of actuators only focus on the actuators themselves, ignoring the disturbance caused by the fault of the actuator position sensor, which may easily lead to wrong decisions. In order to distinguish the actuator fault from its position sensor fault and identify the fault type accurately, an offline diagnosis method of convolutional neural network (CNN) with novel topology for processing time series is proposed. A new shift layer is added after the input layer, which avoids the loss of a large number of features due to the direct connection between the time series and the convolutional layer. A local topological network learning complex pattern with inception module is designed to improve the diagnostic accuracy in different working conditions. The wide residual structure is introduced to expand the convolutional channel, which allows the network features at the bottom level to propagate directly to the top level to prevent network degradation. Simulation results show that this method can accurately diagnose the actuator fault and its position sensor, with an average accuracy of 96.8%. Compared with the current mainstream data-driven methods, the precision and recall are increased by 6.3% and 6.7% respectively.

scholarly journals Fault Diagnosis Method Based on Encoding Time Series and Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 165232-165246
Author(s):  
Chunlin Li ◽  
Jianbin Xiong ◽  
Xingtong Zhu ◽  
Qinghua Zhang ◽  
Shuize Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Diagnosis Method

Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph

2019 ◽  
Vol 196 ◽  
pp. 950-965 ◽  
Author(s):  
Xiaoyang Lu ◽  
Peijie Lin ◽  
Shuying Cheng ◽  
Yaohai Lin ◽  
Zhicong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Photovoltaic Array

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information
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