title: Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.
abstract: Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.
authors.affiliation: College of Information Engineering, Capital Normal University, Beijing 100048, China. zhangran@cnu.edu.cn.|Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China. zhangran@cnu.edu.cn.|Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China. zhangran@cnu.edu.cn.
authors.forename: Ran
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Information Management Department, Beijing Institute of Petrochemical Technology, Beijing 102617, China. zhenpeng@bipt.edu.cn.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Peng
authors.affiliation: College of Information Engineering, Capital Normal University, Beijing 100048, China. wulifeng@cnu.edu.cn.|Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China. wulifeng@cnu.edu.cn.|Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China. wulifeng@cnu.edu.cn.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Wu
authors.affiliation: College of Information Engineering, Capital Normal University, Beijing 100048, China. yaobeibei@cnu.edu.cn.|Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China. yaobeibei@cnu.edu.cn.|Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China. yaobeibei@cnu.edu.cn.
authors.forename: Beibei
authors.initials: B
authors.lastname: Yao
authors.affiliation: College of Information Engineering, Capital Normal University, Beijing 100048, China. guanyong@cnu.edu.cn.|Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China. guanyong@cnu.edu.cn.|Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China. guanyong@cnu.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Guan
keywords: deep neural networks; faults diagnosis; raw sensor data; temporal coherence