scholarly journals Need for Adaptive Signal Processing Technique for Tool Condition Monitoring in Turning Machines

2015 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
J. Emerson Raja ◽  
W.S. Lim ◽  
C. Venkatases ◽  
C. Senthilpar ◽  
S. Purushotha
Keyword(s):  
Signal Processing ◽  
Condition Monitoring ◽  
Adaptive Signal Processing ◽  
Processing Technique ◽  
Tool Condition Monitoring ◽  
Signal Processing Technique ◽  
Tool Condition ◽  
Adaptive Signal

Condition Monitoring of PMSM and Stator-inter Turn Fault Diagnosis by Advanced Signal Processing Technique

2021 ◽  
Vol 23 (07) ◽  
pp. 1419-1430
Author(s):  
Khadim Moin Siddiqui ◽  
◽  
Farhad Ilahi Bakhsh ◽  
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Dynamic Performance ◽  
Processing Technique ◽  
Industrial Applications ◽  
Fault Analysis ◽  
Industrial Plant ◽  
Signal Processing Technique ◽  
Permanent Magnet Synchronous Motors

In the present time, Permanent Magnet Synchronous Motors (PMSMs) are extensively used in many industrial applications due to its advantages over conventional synchronous motor. The PMSM is compact and efficient with high dynamic performance, thus having more advantages such as light weight, small size and bulky burden ability. When PMSMs are failed during the operation then large revenue losses occurs for industries. Hence, it is essential to diagnose these faults before occurring, for protection of any industrial plant. In the paper, firstly a comprehensive review of condition monitoring has been done for PMSM faults and their diagnostics techniques. From review, it is found that the stator inter-turn fault diagnosis has been the challenging task for many researchers. Hence, the work has been extended for fault analysis of stator inter-turn under transient conditions, which is effectively analyzed with the help of advanced signal processing technique.

A Novel Generalized Approach for Real-Time Tool Condition Monitoring

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Mahmoud Hassan ◽  
Ahmad Sadek ◽  
M. H. Attia ◽  
Vincent Thomson
Keyword(s):  
Statistical Analysis ◽  
Condition Monitoring ◽  
High Speed ◽  
Threshold Level ◽  
Processing Technique ◽  
Spindle Motor ◽  
Tool Condition Monitoring ◽  
Important Principle ◽  
Tool Condition ◽  
Cutting Processes

In high-speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial demands. Several studies for TCM were carried out to capture tool failure using complex conventional and artificial intelligence (AI) techniques. However, these studies suffer from the absence of standardization and generalization. Hence, this paper presents a robust and reliable processing technique for the cutting process signals to extract generalized features in time and frequency domains. The proposed technique masks the effects of the cutting conditions on the extracted features and accentuates the tool condition effect. Characterization and statistical analysis of the processed features were performed to examine their sensitivity to the tool condition. The results revealed the processing technique capability to separate the features extracted from the spindle motor current signals into two mutually exclusive clusters according to their tool condition. The statistical analysis results were employed to optimize the tool condition detection approach using linear discrimination analysis (LDA) model. The results indicate the capability of the processing technique to minimize the system learning effort and to detect tool wear above the threshold level with accuracy above 90%.

scholarly journals Tool Condition Monitoring of Single-point Dressing Operation by Digital Signal Processing of AE and AI

Procedia CIRP ◽  
2018 ◽  
Vol 67 ◽  
pp. 307-312 ◽  
Author(s):  
Doriana M. D’Addona ◽  
Salvatore Conte ◽  
Wenderson Nascimento Lopes ◽  
Paulo R. de Aguiar ◽  
Eduardo C. Bianchi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Condition Monitoring ◽  
Single Point ◽  
Digital Signal ◽  
Tool Condition Monitoring ◽  
Tool Condition

Fault Detection and Diagnosis in Gears Using Wavelet Analysis Techniques and Comparison on their Diagnostic Capability

2015 ◽  
Vol 789-790 ◽  
pp. 587-591
Author(s):  
M. Lokesha ◽  
M.C. Majumder ◽  
K.P. Ramachandran
Keyword(s):  
Signal Processing ◽  
Wavelet Analysis ◽  
Condition Monitoring ◽  
Experimental Studies ◽  
Vibration Signal ◽  
Processing Technique ◽  
Fault Detection And Diagnosis ◽  
Signal Processing Technique ◽  
Base Function ◽  
Fault Characterization

The concept of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. Nowadays, wavelet analysis based signal processing technique is applied as effective tool for condition monitoring. The experimental studies were conducted on the gear testing apparatus to obtain the vibration signal from a healthy gear and an induced faulty gear. In this paper, two different techniques using Laplace wavelet as base function are used to characterize the fault in the gear signals, specifically wavelet enveloped power spectrum and wavelet kurtosis. The wavelet parameters are optimized using genetic algorithm to select most fault related features. A comparative study detailing features of fault characterization is also given in order to understand the effectiveness of both the wavelet based signal processing methods and their fault diagnosis capability.

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