Componential coding in the condition monitoring of electrical machines Part 2: application to a conventional machine and a novel machine

This paper (Part 2) presents the practical application of componential coding, the principles of which were described in the accompanying Part 1 paper. Four major issues are addressed, including optimization of the neural network, assessment of the anomaly detection results, development of diagnostic approaches (based on the reconstruction error) and also benchmarking of componential coding with other techniques (including waveform measures, Fourier-based signal reconstruction and principal component analysis). This is achieved by applying componential coding to the data monitored from both a conventional induction motor and from a novel transverse flux motor. The results reveal that machine condition monitoring using componential coding is not only capable of detecting and then diagnosing anomalies but it also outperforms other conventional techniques in that it is able to separate very small and localized anomalies

Detection and Diagnosis of Motor Stator Faults using Electric Signals from Variable Speed Drives

Motor current signature analysis has been investigated widely for diagnosing faults of induction motors. However, most of these studies are based on open loop drives. This paper examines the performance of diagnosing motor stator faults under both open and closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum analysis. Evaluation results show that the stator fault causes an increase in the sideband amplitude of motor current signature only when the motor is under the open loop control. However, the increase in sidebands can be observed in both the current and voltage signals under the sensorless control mode, showing that it is more promising in diagnosing the stator faults under the sensorless control operation

Monitoring and damping unbalanced magnetic pull due to eccentricity fault in induction machines: A review

© 2017 IEEE. Condition monitoring can diagnose the inception of fault mechanisms in induction motors, thus avoiding failure and expensive repairs. Therefore, there is a strong need to develop an efficient condition monitoring. The main target is to achieve a relatively low cost and/or non-invasive system which is still powerful in terms of monitoring for online detection of developing faults. The presented paper addresses rotor eccentricity faults and studies conventional monitoring techniques for induction motors. In order to reduce the unbalanced magnetic pull (UMP) in case of an eccentric rotor, the eccentricity-generated additional airgap flux waves should be reduced. The radial forces in an induction motor are calculated, and the characteristics of unbalanced magnetic pull are described

On-line measurement of partial discharges in high voltage rotating machines.

The on-line condition monitoring of rotating machines is given paramount importance, particularly in Oils and Gas industries where the financial implications of machine shutdown is very high. This project work was directed towards the on-line condition monitoring of high voltage rotating machines by detection of partial discharges (PD) which are indicative of stator insulation degradation. Partial discharge manifests itself in various forms which can be detected using various electrical and non-electrical techniques. The electrical method of detecting small current pulses generated by PD using a Rogowski coil as a sensor has been investigated in this work. Dowding & Mills, who are commercially involved in the condition monitoring of rotating machines, currently use a system called StatorMonotor® for PD detection. The research is intended to develop a new partial discharge detection system that will replace the existing system which is getting obsolete. A three phase partial discharge detection unit was specified, designed and developed that is capable of filtering, amplifying and digitising the discharge signals. The associated data acquisition software was developed using LabVIEW software that was capable of acquiring, displaying and storing the discharge signals. Additional software programs were devised to investigate the removal of external noise. A data compression algorithm was developed to store the discharge data in an efficient manner; also ensuring the backward compatibility to the existing analysis software. Tests were performed in laboratory and on machines on-site and the results are presented. Finally, the data acquisition (DAQ) cards that used the PCMCIA bus was replaced with new USB based DAQ cards with the software modified accordingly. The three phase data acquisition unit developed as a result of this project has produced encouraging results and will be implemented in an industrial environment to evaluate and benchmark its performance with the existing system. Most importantly, a hardware data acquisition platform for the detection of PD pulses has been established within the company which is easily maintainable and expandable to suit any future requirements

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults

Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability