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.

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.

Detection of inappropriate working conditions for the timing belt in internal-combustion engines using vibration signals and data mining

2016 ◽  
Vol 231 (3) ◽  
pp. 418-432 ◽  
Author(s):  
Meghdad Khazaee ◽  
Ahmad Banakar ◽  
Barat Ghobadian ◽  
Mostafa Agha Mirsalim ◽  
Saeid Minaei ◽  
...  
Keyword(s):  
High Temperature ◽  
Frequency Domain ◽  
High Load ◽  
Operating Conditions ◽  
Transform Method ◽  
Time Frequency ◽  
Normal Operating ◽  
Timing Belt ◽  
Temperature Operating ◽  
Abnormal Operating Conditions

Abnormal operating conditions for the timing belt can lead to cracks, fatigue, sudden rupture and damage to engines. In this study, an intelligent system was developed to detect and classify high-load operating conditions and high-temperature operating conditions for timing belts. To achieve this, vibration signals in normal operating conditions, high-load operating conditions and high-temperature operating conditions were collected. Time-domain signals were transformed to the frequency domain and the time–frequency domain using the fast Fourier transform method and the wavelet transform method respectively. In the data-mining stage, 25 statistical features were extracted from different signal domains. The improved distance evaluation method was adopted to select the best features and to reduce the input space for the classifier. Then, the signal features from the time domain, the frequency domain and the time-frequency domain were fed into an artificial neural network to evaluate the accuracy of this designed procedure for detecting inappropriate operating conditions for the timing belt. Based on all these features extracted from the signals in the time, frequency and time–frequency domains, the artificial neural network classifier detected and classified normal operating conditions, high-load operating conditions and high-temperature operating conditions with accuracies of 73.3%, 85% and 89.2% respectively. The classification accuracies using features selected by improved distance evaluation in the signals from the time, frequency and time–frequency domains were found to be 85%, 95.8% and 95% respectively. The results showed that the developed system was capable of detecting and classifying both the normal operating conditions and abnormal operating conditions for the timing belt. The results also suggested that a combination of signal processing and feature selection can significantly enhance the classification accuracy.

scholarly journals Space Lower Bounds for the Signal Detection Problem

2020 ◽  
Author(s):  
Faith Ellen ◽  
Rati Gelashvili ◽  
Philipp Woelfel ◽  
Leqi Zhu
Keyword(s):  
Signal Detection ◽  
Lower Bounds ◽  
Detection Problem

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

LFM Signal Detection Method Based on Fractional Fourier Transform

2014 ◽  
Vol 989-994 ◽  
pp. 4001-4004 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Yu Ming Zhu
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Frequency Analysis ◽  
Experimental Analysis ◽  
Fractional Fourier Transform ◽  
Detection Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Strong Impulse

As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.

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