Multi-Physical Signature Analysis of Induction Machines under Unbalanced Supply Voltage

Mollet, Yves; Pergolesi, Matteo; Sarrazin, Mathieu; Janssens, Karl; Auweraer, Herman Van der; Chiariotti, Paolo; Castellini, Paolo; Gyselinck, Johan

doi:10.1109/icelmach.2018.8506840

Cited by 8 publications

(8 citation statements)

References 17 publications

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“…Another example measures an estimator for I RM S based on two 5 current signals. A fluctuating component at twice the fundamental frequency is observed due to the presence of inverse sequence current in the stator [58].…”

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

“…Motor Current Signature Analysis (MCSA) is a popular class of techniques for detecting motor faults that apply signal processing methods to current signals. It can be used to detect a voltage unbalance [57,58]. Some of these methods require measuring multiple motor current signals.…”

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

“…A single current sensor is sensitive to a secondary effect of the voltage unbalance [58]. The oscillations of the shaft lead to an increase in the third harmonic.…”

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

“…This results in a pulsation of the amplitude of the forces at twice the stator frequency. This causes radial vibrations in the induction motor [58,61,62]. The forces have a tangential component (creating the torque) but also a radial component (the stator flux attracts the rotor like an electromagnet) generating the vibrations.…”

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

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A general anomaly detection framework for fleet-based condition monitoring of machines

Hendrickx

Meert

Mollet

et al. 2020

Mechanical Systems and Signal Processing

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Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial intelligence in an effort to avoid time-consuming manual analysis and handcrafted feature extraction. Many of these only analyze a single machine and require a large historical data set. In practice, this can be difficult and expensive to collect. However, some industrial condition monitoring applications involve a fleet of similar operating machines. In most of these applications, it is safe to assume healthy conditions for the majority of machines. Deviating machine behavior is then an indicator for a machine fault.This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring. It uses generic building blocks and offers three key advantages. First, a historical data set is not required due to online fleet-based comparisons. Second, it allows incorporating domain expertise by user-defined comparison measures. Finally, contrary to most black-box artificial intelligence techniques, easy interpretability allows a domain expert to validate the predictions made by the framework.Two use-cases on an electrical machine fleet demonstrate the applicability of the framework to detect a voltage unbalance by means of electrical and vibration signatures.

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Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

“…A single current sensor is sensitive to a secondary effect of the voltage unbalance [58]. The oscillations of the shaft lead to an increase in the third harmonic.…”

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

Section: Classic Voltage Unbalance Diagnosis Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

A general anomaly detection framework for fleet-based condition monitoring of machines

Hendrickx

Meert

Mollet

et al. 2020

Mechanical Systems and Signal Processing

Self Cite

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“…Once loss of phase happens, it does not immediately cause any damage or failure, however without recognition, it may force equipment to operate under stress, cause overheating and produce errors after a period of time [3]. The authors of [4,6] studied impact on the voltage unbalance on the motor drives and claimed that voltage imbalances as small as 3%, had the ability to increase the motor temperature by up to 25%, but single phasing causes a more significant distortion of voltage.…”

Section: Introductionmentioning

confidence: 99%

Phase Loss Detection Using Current Signals: A Review

Dianov

Anuchin

2021

IEEE Access

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This paper discusses open phase faults, which can happen in a system grid-converter-motor drive, and measures to properly handle this type of fault. The authors of this paper provide classification of algorithms used for the detection of the loss of phase and explain the distinctive features of each group of methods. They review existing algorithms, which are based on the analysis of the current signals, discuss their pros and cons and suggest possible areas of usage for each group of methods. The authors also propose one novel method for detection of open phase, which was developed for low-cost systems with low resolution analog-to digital converters (ADC). This paper mainly considers methods for three-phase motors as the most popular machines, however some algorithms can be used in multiphase drives. The authors of the paper also share their more than 20 years' experience combined, in this area, which was obtained by developing industrial and commercial drives, and focus on the requirements of the IEC/UL 60730 safety standard, where the phase-loss detection algorithm is one of the essential parts of control system.

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Design of an efficient neural network model for detection and classification of phase loss faults for three-phase induction motor

Dawood,

Hasaneen,

Abdel-Aziz

2024

Neural Comput & Applic

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In industrial applications, three-phase induction motors (IMs) are widely used, and they are subjected to many types of faults. One such fault is the loss of a motor phase caused by a blown fuse, a broken wire, mechanical damage, etc. When this fault occurs during motor operation, it continues to rotate but experiences rapid heating, which can ultimately lead to motor failure. Therefore, various protection devices are available to protect the motor against this fault, but most traditional protection devices do not offer a comprehensive classification of such a fault. So, in this paper, an efficient neural network model is presented for detecting and classifying 12 types of phase loss faults for a three-phase induction motor (IM) based on factors such as the unhealthy phase, fault location, and motor action modes (standstill, transient, and steady-state modes). Thus, the main goal of this work is to determine the motor mode during the fault, the defective phase, and its location to help the maintenance team repair the fault quickly. The system is simulated and tested using the “MATLAB/Simulink” software, employing a feed-forward neural network. The simulation results demonstrate that the proposed network achieves correct detection and classification of phase loss faults within a short time frame from the occurrence of the fault. Therefore, the proposed network model proves to be a simple and reliable solution for integration into the protection system of a three-phase IM, enabling the detection and classification of various phase loss faults.

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Multi-Physical Signature Analysis of Induction Machines under Unbalanced Supply Voltage

Cited by 8 publications

References 17 publications

A general anomaly detection framework for fleet-based condition monitoring of machines

A general anomaly detection framework for fleet-based condition monitoring of machines

Phase Loss Detection Using Current Signals: A Review

Design of an efficient neural network model for detection and classification of phase loss faults for three-phase induction motor

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