Fault diagnosis with classification methods

2006 ◽  
pp. 295-310 ◽  
Author(s):  
Rolf Isermann
Keyword(s):  
Fault Diagnosis ◽  
Classification Methods

Sensor Fault Diagnosis Based on SOFM Neural Network

2014 ◽  
Vol 511-512 ◽  
pp. 193-196
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Accuracy ◽  
Classification Methods ◽  
Statistical Classification ◽  
Sensor Fault ◽  
Discriminant Functions ◽  
Fast Speed ◽  
Diagnosis Method ◽  
Simulation Results

Traditional sensor fault diagnosis is mainly based on statistical classification methods. The discriminant functions in these methods are extremely complex, and typical samples of reference modes are not easy to get, therefore it is difficult to meet the actual requirements of a project. In view of the deficiencies of conventional sensor fault diagnosis technologies, a fault diagnosis method based on self-organizing feature map (SOFM) neural network is presented in this paper. And it is applied to the fault diagnosis of pipeline flow sensor in a dynamic system. The simulation results show that the fault diagnosis method based on SOFM neural network has a fast speed, high accuracy and strong generalization ability, which verifies the practicality and effectiveness of the proposed method.

Comparative evaluation of classification methods used in fault diagnosis of industrial processes

2013 ◽  
Vol 11 (2) ◽  
pp. 682-689 ◽  
Author(s):  
Alberto Prieto Moreno ◽  
Orestes Llanes Santiago ◽  
Jose Manuel Bernal de Lazaro ◽  
Emilio Garcia Moreno
Keyword(s):  
Fault Diagnosis ◽  
Comparative Evaluation ◽  
Industrial Processes ◽  
Classification Methods

scholarly journals Fault Diagnosis of Car Engine by Using a Novel GA-Based Extension Recognition Method

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Meng-Hui Wang ◽  
Pi-Chu Wu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Experimental Results ◽  
Classification Methods ◽  
Recognition Method ◽  
Important Work ◽  
Industrial Apparatus ◽  
Fault Recognition ◽  
Traditional Classification ◽  
Hidden Fault

Due to the passenger’s security, the recognized hidden faults in car engines are the most important work for a maintenance engineer, so they can regulate the engines to be safe and improve the reliability of automobile systems. In this paper, we will present a novel fault recognition method based on the genetic algorithm (GA) and the extension theory and also apply this method to the fault recognition of a practical car engine. The proposed recognition method has been tested on the Nissan Cefiro 2.0 engine and has also been compared to other traditional classification methods. Experimental results are of great effect regarding the hidden fault recognition of car engines, and the proposed method can also be applied to other industrial apparatus.

scholarly journals Acoustic-Based Fault Diagnosis of Commutator Motor

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 299 ◽  
Author(s):  
Adam Glowacz
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Acoustic Analysis ◽  
Recognition Rate ◽  
Gear Train ◽  
Self Organizing Map ◽  
Nearest Neighbour ◽  
Classification Methods ◽  
Backpropagation Neural Network ◽  
Selection Of

In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.

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