Generalized harmonic wavelet as an adaptive filter for machine health diagnosis

2005 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao
Keyword(s):  
Adaptive Filter ◽  
Harmonic Wavelet ◽  
Machine Health ◽  
Health Diagnosis

scholarly journals Non-Invasive Inspections: A Review on Methods and Tools

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8474
Author(s):  
Mubarak Alotaibi ◽  
Barmak Honarvar Shakibaei Asli ◽  
Muhammad Khan
Keyword(s):  
Flux Analysis ◽  
Full Potential ◽  
Non Invasive ◽  
Shaft Misalignment ◽  
Machine Health ◽  
Radar Sensing ◽  
Definition Of ◽  
Alternative Current ◽  
Tools And Methods ◽  
Health Diagnosis

Non-Invasive Inspection (NII) has become a fundamental tool in modern industrial maintenance strategies. Remote and online inspection features keep operators fully aware of the health of industrial assets whilst saving money, lives, production and the environment. This paper conducted crucial research to identify suitable sensing techniques for machine health diagnosis in an NII manner, mainly to detect machine shaft misalignment and gearbox tooth damage for different types of machines, even those installed in a hostile environment, using literature on several sensing tools and techniques. The researched tools are critically reviewed based on the published literature. However, in the absence of a formal definition of NII in the existing literature, we have categorised NII tools and methods into two distinct categories. Later, we describe the use of these tools as contact-based, such as vibration, alternative current (AC), voltage and flux analysis, and non-contact-based, such as laser, imaging, acoustic, thermographic and radar, under each category in detail. The unaddressed issues and challenges are discussed at the end of the paper. The conclusions suggest that one cannot single out an NII technique or method to perform health diagnostics for every machine efficiently. There are limitations with all of the reviewed tools and methods, but good results possible if the machine operational requirements and maintenance needs are considered. It has been noted that the sensors based on radar principles are particularly effective when monitoring assets, but further comprehensive research is required to explore the full potential of these sensors in the context of the NII of machine health. Hence it was identified that the radar sensing technique has excellent features, although it has not been comprehensively employed in machine health diagnosis.

Time-Frequency Manifold as a Signature for Machine Health Diagnosis

2012 ◽  
Vol 61 (5) ◽  
pp. 1218-1230 ◽  
Author(s):  
Qingbo He ◽  
Yongbin Liu ◽  
Qian Long ◽  
Jun Wang
Keyword(s):  
Time Frequency ◽  
Machine Health ◽  
Health Diagnosis

Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Structural Defects ◽  
Intrinsic Mode Functions ◽  
Rotary Machine ◽  
Mode Decomposition ◽  
Machine Health ◽  
Characteristic Features ◽  
Health Diagnosis

This paper presents a signal decomposition and feature extraction technique for the health diagnosis of rotary machines, based on the empirical mode decomposition. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMF for extracting defect-induced characteristic features out of vibration signals. The envelope spectrum of the selected IMF is investigated as an indicator for both the existence and the specific location of structural defects within the bearing. Theoretical foundation of the technique is introduced, and its performance is experimentally verified.

scholarly journals Transfer Learning with Pretrained Neural Network Between Unrelated Tasks for Machine Health Diagnosis

2020 ◽  
Vol 8 (6) ◽  
pp. 2715-2720
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transfer Learning ◽  
Input Data ◽  
Operating Conditions ◽  
The Face ◽  
Industrial Maintenance ◽  
Machine Health ◽  
High Degree ◽  
Health Diagnosis

Deep Learning (DL) has contributed a lot in the field of industrial maintenance, in particular predictive maintenance by detecting potential failures and breakdowns before their appearance. Unfortunately, the DL has some limitations like the need for a large amount of data to produce an effective prediction model and also the fragility of the model in the face of changes in operating conditions. Another approach, the Transfer Learning (TL), had demonstrated in the literature that he can overcome these weaknesses. In this article, we will be using this technique with the pretrained neural network, AlexNet, which had been previously trained with the ImageNet database. Our method doesn’t require a high amount of input data and thus saves a lot of time in retraining the network in another task, which can be related or unrelated to the source task. In fact, the prediction model was successfully adapted to the bearings diagnosis case. It showed also high degree of robustness against changes of functioning conditions.

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