Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.

Condition Monitoring and Fault Diagnosis of Electrical Motors&#8212;A Review

A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. To determine the conditions of the stator insulation system of motor drive systems, various testing and monitoring methods have been developed. This paper presents an in-depth literature review of testing and monitoring methods, categorizing them into online and offline methods, each of which is further grouped into specific areas according to their physical nature. The main focus of this paper is on testing and monitoring techniques that diagnose the condition of the turn-to-turn insulation of low-voltage machines, which is a rapidly expanding area for both research and product development efforts. In order to give a compact overview, the results are summarized in two tables. In addition to monitoring methods on turn-to-turn insulation, some of the most common methods to assess the stator's phase-to-ground and phase-to-phase insulation conditions are included in the tables as well.

A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low-Voltage Induction Machines Focusing on Turn Insulation Problems

Increasing interest has been seen in condition monitoring (CM) techniques for electrical equipment, mainly including transformer, generator, and induction motor in power plants, because CM has the potential to reduce operating costs, enhance the reliability of operation, and improve power supply and service to customers. Literature is accumulated on developing intelligent CM systems with advanced practicability, sensitivity, reliability, and automation. A literature survey is felt necessary with an aim to reflect the state-of-the-art development in this important area. After introducing the concepts and functions of CM, this paper describes the popular monitoring methods for and research status of CM on transformers, generators, and induction motors, respectively. The paper also points out the potential benefits through the utilization of advanced signal processing and artificial intelligence techniques in developing novel CM schemes.

Condition monitoring techniques for electrical equipment-a literature survey

A monitoring system for a heating, ventilation, and air conditioning (HVAC) system of a building includes a monitoring device installed at the building, a monitoring server located remotely from the building, and a review server. The monitoring device measures an aggregate current supplied to components of the HVAC system and transmits current data based on the measured aggregate current. The monitoring server receives the transmitted current data and, based on the received current data, assesses whether failures have occurred in first and second components of the HVAC components. The monitoring server generates a first advisory in response to determining that the failure has occurred in the first component. The review server provides the first advisory to a technician for review and, in response to the technician verifying that the failure has occurred, transmits a first alert.

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Hvac system remote monitoring and diagnosis

As evidenced by industrial surveys, stator-related failures account for a large percentage of faults in induction machines. The objective of this paper is to provide a survey of existing techniques for detection of stator-related faults, which include stator winding turn faults, stator core faults, temperature monitoring and thermal protection, and stator winding insulation testing. The root causes of fault inception, available techniques for detection, and recommendations for further research are presented. Although the primary focus is online and sensorless methods that use machine voltages and currents to extract fault signatures, offline techniques such as partial discharge detection are also examined. Condition monitoring, fault diagnostics, insulation testing, interlaminar core faults, partial discharge (PD), temperature monitoring, turn faults.

A Survey of Methods for Detection of Stator-Related Faults in Induction Machines

Systems and methods are disclosed for controlling, diagnosing and prognosing the health of a motorized system. The systems may comprise a diagnostics system, a prognostic system and a controller, wherein the diagnostics system and/or prognostic system employs a neural network, an expert system, and/or a data fusion component in order to assess and/or prognose the health of the motorized system according to one or more attributes associated therewith. The controller may operate the motorized system in accordance with a setpoint and/or a diagnostics signal from the diagnostics system and/or prognostic information. Also disclosed are methodologies for controlling, diagnosing and prognosing the health of a motorized system, comprising operating a motor in the motorized system in a controlled fashion, diagnosing and/or prognosing the health of the motorized system according to a measured attribute associated with the motorized system, wherein the motor may be operated according to a setpoint and/or the diagnostics signal and/or prognosis.

Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis

A system and method is provided for monitoring the operating condition of a pump by evaluating fault data encoded in the instantaneous current of the motor driving the pump. The data is converted to a frequency spectrum which is analyzed to create a fault signature having fault attributes relating to various fault conditions associated with the pump. The fault signature is then input to a neural network that operates in conjunction with a preprocessing and post processing module to perform decisions and output those decisions to a user interface. A stand alone module is also provided that includes an adaptive preprocessing module, a one-shot unsupervised neural network and a fuzzy based expert system to provide a decision making module that operates with limited human supervision.

Detection of pump cavitation/blockage and seal failure via current signature analysis

A new method of obtaining diagnostic data from induction motors, derived from space vector angular fluctuation, is presented in this paper. It obtains data from stator current by exploring the position of the current space vector in the space vector plane. Variations of the phase angle of the vector are subjected to Fourier transformation and analysed in the frequency domain. The method has been tested on laboratory induction motors with simulated stator faults, rotor resistance unbalance, supply voltage unbalance, as well as with computer simulation. The Goertzel algorithm is employed as a tool for real-time fault monitoring of the frequency domain. In the case of a stator fault, which can develop rapidly, this allows diagnosis within a few cycles. As well as providing a new tool in motor diagnostics, this method is able to detect both stator and rotor faults, and gives a good basis for an integrated induction motor condition monitor.

Induction motor fault detection by space vector angular fluctuation

Systems and methodologies are disclosed for detecting faults and adverse conditions associated with electric motors. The method comprises obtaining a current signal associated with the motor, calculating a space vector from the current signal, determining a space vector angular fluctuation from the space vector, and analyzing the space vector angular fluctuation in order to detect at least one fault associated with the motor. Systems are disclosed having a diagnostics component adapted to obtain a space vector angular fluctuation from a current signal relating to operation of the motor, and to analyze the space vector angular fluctuation in order to detect at least one fault in the motor.

System and method for motor fault detection based on space vector angular fluctuation

A power decomposition technique (PDT) has been used to derive positive and negative sequence components of arbitrary three phase signals in the time domain, in order to detect deterioration in the stator of induction motors. Negative sequence currents arising from supply unbalance are calculated using the reactive component of motor negative sequence impedance, which is essentially independent of temperature. The effects of variation in load, supply voltage, and supply voltage unbalance, have been eliminated by using a recursive least squares method to characterize the associated variation in motor reactance and residual negative sequence current in the healthy motor. Compensation is also applied for nonsimultaneous current sampling of motor fault current. Negative sequence fault currents as low as 0.1 percent of full load current can be detected in the presence of normal variations in load, voltage, and unbalance.

Peter J. Unsworth

Papers

Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis

Detection of pump cavitation/blockage and seal failure via current signature analysis

Induction motor fault detection by space vector angular fluctuation

System and method for motor fault detection based on space vector angular fluctuation

Stator fault diagnosis in induction motors using power decomposition