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.

Diagnosis of the Joint Backlash of a Mechanism: Wigner-Ville Distribution Combined With Correlation Techniques

1995 ◽  
Author(s):  
M.-C. Pan ◽  
B. Verbeure ◽  
H. Van Brussel ◽  
P. Sas
Keyword(s):  
Signal Detection ◽  
Operating Conditions ◽  
Degree Of Freedom ◽  
Two Dimensional ◽  
Detection Problem ◽  
Time Frequency ◽  
Normal Operating ◽  
Link Mechanism ◽  
Pattern Differentiation ◽  
Correlation Techniques

Abstract 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 various degrees of single-joint backlash. The method also allows to detect and to single out backlash present in two joints of a multi-link mechanism. In the work reported here, the Wigner-Ville distribution based signal detection and the generalized symmetrical Itakura distance were proposed as tools for pattern differentiation. Initially the proposed methods have been applied to quantify the backlash of a single joint. Consecutively, the detection problem has been generalized to diagnose faults in two joints simultaneously. Due to the extra degree of freedom given by the 2D nature of the WVD’s, certain time-frequency regions were chosen as reference signatures in the case of single-joint backlash, and the signatures, spanning over two impact transients at the reverses of motion, were chosen in the cases of double-joint backlash. The proposed techniques have been successfully implemented on a two-link mechanism.

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.

Contact characteristic and vibration mechanism of rolling element bearing in the process of fault evolution

2021 ◽  
Vol 235 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Wenbing Tu ◽  
Jinwen Yang ◽  
Wennian Yu ◽  
Ya Luo
Keyword(s):  
Fault Diagnosis ◽  
Vibration Response ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Vibration Mechanism ◽  
Contact Characteristic ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Fault Evolution

The vibration response of rolling element bearing has a close relation with its fault. An accurate evaluation of the bearing vibration response is essential to the bearing fault diagnosis. At present, most bearing dynamics models are built based on rigid assumptions, which may not faithfully reveal the dynamic characteristics of bearing in the presence of fault. Moreover, previous similar works mainly focus on the fault with a specified size without considering the varying contact characteristics as the fault evolves. This paper developed an explicit dynamics finite element model for the bearing with three types of raceway faults considering the flexibility of each bearing component in order to accurately study the contact characteristic and vibration mechanism of defective bearings in the process of fault evolution. The developed model is validated by comparing its simulation results with both analytical and experimental results. The dynamic contact patterns between the rolling elements and the fault, the additional displacement due to the fault and the faulty characteristics within the bearing vibration signal during the fault evolution process are investigated. The analysis results from this work can provide practitioners an in-depth understanding towards the internal contact characteristics with the existence of raceway fault and theoretical basis for rolling bearing fault diagnosis.

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

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

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
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