scholarly journals Health Condition Monitoring of Induction Motors

10.5772/61110 ◽  
2015 ◽  
Author(s):  
Wilson Wang ◽  
Derek Dezhi Li
Keyword(s):  
Condition Monitoring ◽  
Induction Motors ◽  
Health Condition

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Automatic Condition Monitoring and Early Fault Detection of Gearbox

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Chengdong Wang ◽  
Jingqi Xiong
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Life Time ◽  
Health Condition ◽  
Evaluation Index ◽  
Dynamic Threshold ◽  
Time Data ◽  
Machinery Condition Monitoring ◽  
Maintenance Program ◽  
Gear Fault

Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.

Modeling and Parameter Identification for Condition Monitoring of Surface-Mount Permanent Magnet Machines Under Magnet Demagnetization

2020 ◽  
Author(s):  
Fanny Pinto Delgado ◽  
Ziyou Song ◽  
Heath F. Hofmann ◽  
Jing Sun
Keyword(s):  
Permanent Magnet ◽  
Condition Monitoring ◽  
High Performance ◽  
High Efficiency ◽  
Health Condition ◽  
Maintenance Cost ◽  
Parameter Estimator ◽  
Two Phase ◽  
Machine Model ◽  
Surface Mount

Abstract Permanent Magnet Synchronous Machines (PMSMs) have been preferred for high-performance applications due to their high torque density, high power density, high control accuracy, and high efficiency over a wide operating range. During operation, monitoring the PMSM’s health condition is crucial for detecting any anomalies so that performance degradation, maintenance/downtime costs, and safety hazards can be avoided. In particular, demagnetization of PMSMs can lead to not only degraded performance but also high maintenance cost as they are the most expensive components in a PMSM. In this paper, an equivalent two-phase model for surface-mount permanent magnet (SMPM) machines under permanent magnet demagnetization is formulated and a parameter estimator is proposed for condition monitoring purposes. The performance of the proposed estimator is investigated through analysis and simulation under different conditions, and compared with a parameter estimator based on the standard SMPM machine model. In terms of information that can be extracted for fault diagnosis and condition monitoring, the proposed estimator exhibits advantages over the standard-model-based estimator as it can differentiate between uniform demagnetization over all poles and asymmetric demagnetization between north and south poles.

scholarly journals Condition Monitoring of Blade in Turbomachinery: A Review

2014 ◽  
Vol 6 ◽  
pp. 210717 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
Lim Meng Hee ◽  
M. S. Leong ◽  
Salah Al-Obaidi
Keyword(s):  
Condition Monitoring ◽  
Fatigue Failure ◽  
Signal Analysis ◽  
Health Condition ◽  
Diagnostic Methods ◽  
Monitoring Techniques ◽  
Analysis Methods

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Bond Graph Modelling for Condition Monitoring of Induction Motors

2020 ◽  
pp. 511-523
Author(s):  
Aisha Alashter ◽  
Yunpeng Cao ◽  
Khalid Rabeyee ◽  
Samir Alabied ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Induction Motors ◽  
Bond Graph
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