Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

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

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/07301 ◽

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.

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The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis

Nondestructive Evaluation ◽

10.1115/imece2003-55318 ◽

2003 ◽

Cited By ~ 1

Author(s):

Peter W. Tse ◽

Jinyu Zhang

Keyword(s):

Fault Diagnosis ◽

Blind Source Separation ◽

Condition Monitoring ◽

Source Separation ◽

Simulated Data ◽

Vibration Signal ◽

Advantages And Disadvantages ◽

Machine Fault Diagnosis ◽

Machine Fault ◽

Mechanical Components

Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.

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Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a3173.1011121 ◽

2021 ◽

Vol 11 (1) ◽

pp. 174-180

Author(s):

Jyothi R ◽

◽

Tejas Holla ◽

Umesh NS ◽

K Uma Rao ◽

...

Keyword(s):

Fault Diagnosis ◽

Induction Motor ◽

Condition Monitoring ◽

Industrial Applications ◽

Voltage Source ◽

Variable Frequency ◽

Process Industries ◽

Ac Drives ◽

Stator Current ◽

Variable Frequency Drives

AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.

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