This report presents a survey of the reliability of squirrel cage motors on board drilling, production, and other platforms offshore, together with cage motors in the petrochemical industry, gas terminals, and refineries onshore. Most of the activity in this connection is related to The North Sea that offers a tough environment for motors. The collected data have been treated statistically, and the faults sorted according to supply and motor data, driving conditions, electrical protection, maintenance, and so on, and further analyzes failure-initiators, contributors, and underlying causes. Comparisons between this survey and a survey from the IEEE Motor Reliability Working Group [8] have been done. The report also pays some attention to methods for monitoring of machinery and detecting of faults. >

A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals and oil refineries

This paper provides a comprehensive list of books, workshops, conferences, and journal papers related to induction motors faults detection and diagnosis.

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Bibliography on induction motors faults detection and diagnosis

This paper gives a synopsis of condition monitoring methods, both as a diagnostic tool and as a technique for failure identification in high-voltage induction motors in industry. New operating experience data for 483 motor units consisting of 6135 unit years are registered and processed statistically to ascertain the connection between motor data, protection and condition monitoring methods, maintenance philosophy and different types of failures. The different types of failures are further analyzed to determine the failure initiators, contributors and underlying causes. The results have been compared with those of a previous survey, the "IEEE Report of Large Motor Reliability Survey of Industrial and Commercial Installations, 1985." In the present survey, the motors range from 100 to 1300 kW; 47% of them are between 100-500 kW.

Failure identification and analysis for high-voltage induction motors in the petrochemical industry

Apparatus which automates the collection of motor temperature, flux and shaft current data for proactive motor maintenance. Motor speed is determined by an analysis of the flux frequency spectrum, and motor load is determined based on speed, from which a normalized thermal parameter is then determined. From a spectrum analysis of magnetic flux signals a set of peaks each characterized by a frequency and an amplitude is found, and a subset of peaks is identified in accordance with predetermined identity criteria for purposes of comparison. Temperature, magnetic flux and shaft current data are trended to provide early indications of degradation, as well as for recognizing faults.

Proactive motor monitoring for avoiding premature failures and for fault recognition

A method for nonintrusive detection of anomalies in electric machines. A flux coil is positioned external to the motor to obtain magnetic leakage flux measurements, which are transformed to the frequency domain to obtain a flux spectrum. Asymmetry-producing faults, such as turn-to-turn stator winding shorts, produce changes in the amplitudes of individual frequencies and groups of frequencies within a defined slot pass family of frequencies of the flux spectrum. The measured amplitudes of the slot pass family of frequencies are compared to reference and trend data. Significant changes in the slot pass frequency amplitudes indicate the presence of a machine fault or other anomalous condition. Significant changes are determined by comparing individual amplitudes and groups of amplitudes of the slot pass family of frequencies to an absolute or delta threshold value. Alternatively, measured amplitudes of the slot pass frequencies can be statistically compared to trend data previously acquired for the motor to determine when a significant change in the slot pass amplitudes has occurred.

Machine fault detection using slot pass frequency flux measurements

Failure identification of offshore induction motor systems using on-condition monitoring

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Failure identification of offshore induction motor systems using on-condition monitoring