scholarly journals Fault detection and diagnosis method of diesel engine by combining rule-based algorithm and Bayesian/neural networks

2020 ◽  
Author(s):  
Baoping Cai ◽  
Xiutao Sun ◽  
Jiaxing Wang ◽  
Yonghong Liu ◽  
Chao Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Fault Detection And Diagnosis ◽  
Normal Operation ◽  
Fault Identification ◽  
Stable Operation ◽  
Bp Neural Networks ◽  
Rule Based ◽  
Feature Values

Abstract The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring and identification of faults are of great significance. At present, the fault research on diesel engine still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a constant speed. Therefore, a rule-based algorithm for fault diagnosis is proposed. Bayesian networks (BNs) and BP neural networks are used to identify seven faults at different speeds. Changchai EV80 diesel engine is taken as an example, and the feature values are extracted from the vibration signals measured from the cylinder head. The signals are processed by wavelet threshold de-noising and Ensemble Empirical Mode Decomposition (EEMD). The signal-sensitive feature values extracted from the decomposed Intrinsic Mode Function are used to distinguish different faults. After obtaining the feature values, a rule-based algorithm using IF... THEN's logic statement is proposed. BNs and BP neural networks established by parameter learning method are used for fault identification. Furthermore, this paper considers the uncertain factors and the interference of the external environment. Gaussian white noise is added to the raw signal and external excitation interference is applied to the diesel engine when it is running under normal operation condition. The results show that the proposed fault diagnostic method can accurately identify the faults.

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.

Main Converter Fault Diagnosis for Power Locomotive Based on PSO-BP Neural Networks

2011 ◽  
Vol 267 ◽  
pp. 271-276 ◽  
Author(s):  
Hong Sheng Su
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Local Extremum ◽  
Convergence Speed ◽  
Bp Algorithm ◽  
Bp Neural Networks ◽  
Training Samples ◽  
Fault Diagnosis System ◽  
Two Phases

To aim at conventional BP learning algorithm of its flaws, say, low convergence speed and easy falling into local extremum, and etc, during main converter fault diagnosis system for power locomotive, this paper proposed a novel learning algorithm called PSO-BP neural networks based on particle swarm optimization (PSO) and BP neural networks. The algorithm generated the two phases: one is that PSO was applied to optimize the weight values of neural networks based on training samples, the other is that BP algorithm was applied to farther optimize based on verifying samples till the best weight values are achieved. Eventually, a practical example indicates that the proposed algorithm has quick convergence speed and high accuracy, and is ideal patter classifier.

Diagnosis of process faults with neural networks and principal component analysis

2000 ◽  
Vol 214 (2) ◽  
pp. 131-143 ◽  
Author(s):  
J B Gomm ◽  
M Weerasinghe ◽  
D Williams
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Process Variable ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Processing Plant ◽  
Large Network

Industrial plants often have many process variable measurements available, which can be monitored for fault detection and diagnosis. Using all these variables as inputs to an artificial neural network for fault diagnosis can result in an impractically large network, with consequent long training times and high computational requirement during use. Principal component analysis (PCA) is investigated in this paper for generating a reduced number of variables to be used as neural network inputs for fault diagnosis. The main application described is to a real industrial nuclear fuel processing plant. A simulated chemical process was also used to assist the development of the techniques. Results in both applications demonstrate satisfactory fault diagnosis performance with a reduction in the number of neural network parameters of approximately 50 per cent using PCA. The paper also includes some introductory material on PCA and neural networks, and their application to process fault diagnosis.

scholarly journals Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-30
Author(s):  
Wei Cui ◽  
Guoying Meng ◽  
Aiming Wang ◽  
Xinge Zhang ◽  
Jun Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Safe Production ◽  
Continuous Progress ◽  
Machinery Fault Diagnosis

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

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