Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column

2014 ◽  
Vol 92 (3) ◽  
pp. 215-223 ◽  
Author(s):  
Yahya Chetouani
Keyword(s):  
Model Selection ◽  
Fault Detection ◽  
Decision Theory ◽  
Distillation Column ◽  
Detection Problem ◽  
Detection Approach ◽  
Theory Application ◽  
Changes Detection ◽  
Bayes Decision Theory

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Data-Driven Classifier for Extreme Outage Prediction Based on Bayes Decision Theory

2021 ◽  
pp. 1-1
Author(s):  
Mohammad Shahidehpour ◽  
Mostafa Mohammadian ◽  
Farrokh Aminifar ◽  
Nima Amjady
Keyword(s):  
Decision Theory ◽  
Data Driven ◽  
Bayes Decision Theory

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

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