Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

2020 ◽  
Vol 69 (2) ◽  
pp. 509-520 ◽  
Author(s):  
Gaowei Xu ◽  
Min Liu ◽  
Zhuofu Jiang ◽  
Weiming Shen ◽  
Chenxi Huang
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Diagnosis Method

scholarly journals A new bearing fault diagnosis method based on modified convolutional neural networks

2020 ◽  
Vol 33 (2) ◽  
pp. 439-447 ◽  
Author(s):  
Jiangquan ZHANG ◽  
Yi SUN ◽  
Liang GUO ◽  
Hongli GAO ◽  
Xin HONG ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2750 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Xuebing Xu ◽  
Xinyu Shao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Data Driven ◽  
Sensor Data ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
S Transform ◽  
Diagnosis Method

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.

scholarly journals A Non-Contact Fault Diagnosis Method for Rolling Bearings Based on Acoustic Imaging and Convolutional Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132761-132774 ◽  
Author(s):  
Ran Wang ◽  
Fengkai Liu ◽  
Fatao Hou ◽  
Weikang Jiang ◽  
Qilin Hou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Acoustic Imaging ◽  
Rolling Bearings ◽  
Diagnosis Method

scholarly journals An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4358
Author(s):  
Huanyue Liao ◽  
Wenjian Cai ◽  
Fanyong Cheng ◽  
Swapnil Dubey ◽  
Pudupadi Balachander Rajesh
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Hvac Systems ◽  
Data Driven ◽  
Stable Operation ◽  
Online Data ◽  
Rule Based ◽  
Air Handling Units ◽  
Diagnosis Method

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system’s historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document