Qualitative Modeling for Fault Diagnosis Based on Physical Knowledge and Historical Operation Data under Normal Operating Conditions

2020 ◽  
Vol 53 (12) ◽  
pp. 771-786
Author(s):  
Junqing Xia ◽  
Yoshiyuki Yamashita
Keyword(s):  
Fault Diagnosis ◽  
Operating Conditions ◽  
Qualitative Modeling ◽  
Normal Operating ◽  
Physical Knowledge

Fault Diagnosis of Joint Backlash

1998 ◽  
Vol 120 (1) ◽  
pp. 13-24 ◽  
Author(s):  
M.-C. Pan ◽  
H. Van Brussel ◽  
P. Sas ◽  
B. Verbeure
Keyword(s):  
Fault Diagnosis ◽  
Signal Detection ◽  
Industrial Robot ◽  
Vibration Response ◽  
Operating Conditions ◽  
Two Dimensional ◽  
Normal Operating ◽  
Link Mechanism ◽  
Pattern Differentiation ◽  
Correlation Techniques

The aim of this paper is to develop appropriate techniques to detect and classify the joint backlash of a robot by monitoring its vibration response during normal operating conditions. In this investigation, Wigner-Ville distributions combined with two-dimensional correlation techniques have been employed to diagnose the joint faults of multi-link robots. In the study reported here, signal detection based on the Wigner-Ville distribution is proposed as a tool for pattern differentiation. To evaluate the performance of different detection procedures, the detection of a simulated impact transient embedded in three simulated observed signals is presented. To assess the validity of the proposed approaches, they have been successfully employed in the fault diagnosis of link-joints on both a two-link mechanism and an industrial robot.

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

scholarly journals Structural assessment of the EU-DEMO WCLL Central Outboard Blanket segment under normal and off-normal operating conditions

2021 ◽  
Vol 167 ◽  
pp. 112350
Author(s):  
Ilenia Catanzaro ◽  
Pietro Arena ◽  
Salvatore Basile ◽  
Gaetano Bongiovì ◽  
Pierluigi Chiovaro ◽  
...  
Keyword(s):  
Operating Conditions ◽  
Structural Assessment ◽  
Normal Operating ◽  
The Eu

Analysis of Pellet-to-Cladding Gap Closure for a Metallic Micro-cell Pellet under Normal Operating Conditions

2021 ◽  
pp. 153186
Author(s):  
Yang-Hyun Koo ◽  
Jae-Ho Yang ◽  
Dong-Seok Kim ◽  
Dong-Joo Kim ◽  
Chang-Hwan Shin ◽  
...  
Keyword(s):  
Operating Conditions ◽  
Normal Operating ◽  
Gap Closure ◽  
Cell Pellet

scholarly journals Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 173
Author(s):  
Syed Muhammad Tayyab ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Research Study ◽  
High Load ◽  
Operating Conditions ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Spiral Bevel ◽  
Level Identification

Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out.

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