Classical Motor Current Signature Analysis Research Articles

Advanced Analysis of Motor Currents for the Diagnosis of the Rotor Condition in Electric Motors Operating in Mining Facilities

Predictive maintenance of electric motors is a topic of increasing importance in many industrial applications. The mining industry is not an exception; many electric motors operating in mining facilities are critical machines, and their unexpected failures may imply significant losses and can be hazardous for the users. Due to these facts, an increasing research effort has been dedicated to investigate new techniques that are able to provide a reliable diagnostic of the motor condition. Over recent years, monitoring of electrical quantities (e.g., motor currents) has emerged as a very attractive option for determining the health of several motor parts (rotor, eccentricities, bearings) due to its very interesting advantages: possibility of remote motor monitoring, noninvasive nature, simple application, broad fault coverage, etc. The traditional methods based on the analysis of motor currents during a steady-state operation [motor current signature analysis (MCSA)] are being complemented when not replaced by more reliable approaches. This paper applies an innovative transient-based methodology to several case studies referred to large motors operating in mining facilities. The results prove how this modern methodology enables us to overcome some important drawbacks of the classical MCSA, such as its unsuitability under varying speed conditions, and may provide an earlier indication of rotor electrical asymmetries under such working conditions.

Reliable Detection of Rotor Winding Asymmetries in Wound Rotor Induction Motors via Integral Current Analysis

Current analysis has been widely employed in academy and industry for the diagnosis of rotor damages in cage induction motors. The conventional approach based on the fast Fourier transform analysis of steady-state current [motor current signature analysis (MCSA)] has been recently complemented with the development of alternative techniques that rely on the time-frequency analysis of transient quantities of the machine. These techniques may bring important advantages that are related to the avoidance of eventual false indications provided by the classical MCSA. Moreover, their application is also suitable for variable speed conditions. However, the application of current-based methodologies to wound rotor induction motors (WRIM) has been much less studied and, hence, their validation in field WRIM is scarce. This work proposes the application of an integral methodology based on the analysis of both stationary and transient currents for the diagnosis of winding asymmetries in WRIM. The method, based on up to five different fault evidences, is validated in laboratory motors and it is subsequently applied to a large field motor (1500 kW) that was showing signs of abnormal rotor functioning. The results prove that the method is of interest for the field since it helps to ratify without ambiguity the existence of eventual asymmetries in the rotor windings, with no interference with the machine operation. However, due to the complex constructive nature of the rotor winding as well as the presence of auxiliary systems (slip rings, brushes, contactors, etc.), once the fault presence is detected, it may be interesting the utilization of complementary tools to accurately locate the root cause of the asymmetry.

A Simplified Frequency-Domain Detection of Stator Turn Fault in Squirrel-Cage Induction Motors Using an Observer Coil Technique

This paper presents the distinct advantages of stator fault detection using a novel radial flux sensing technique. Intensive research efforts in the past have been focused on the flux signature analysis as they have been more efficient compared to classical motor current signature analysis. In this paper, it is observed that a flux sensing simple internal observer coil (OC) is more succinct than external flux sensors. The proposed simplified, inexpensive technique has yet not been reported in the literature. The new results of root mean square (RMS), fundamental, third harmonic, and total harmonic distortion analysis of the voltage spectra of the OCs are found to exhibit variations in relation to the severity of interturn faults as compared to the healthy condition. The mathematical theory of the technique is provided and supported by an experimental validation. An experiment for confirming the performance of the proposed method was done on a 2.2-kW three-phase four-poles, 440-V squirrel-cage induction motor, increasing the fault level in steps from 1% to 15% combined with variations in load from no load to full load.

Online Stator Interturn Short Circuits Monitoring in the DFOC Induction-Motor Drive

This paper deals with monitoring of interturn short circuits of induction-motor (IM) stator windings operating under the direct field-oriented control (DFOC). Time transients are presented to illustrate the performance of the field-oriented speed control under the stator-winding damage. Novel detection algorithm is introduced, based on an analysis of the internal signals from the control structure: controller outputs and control path decoupling variables. The spectral analysis of these signals is used to detect the stator-winding incipient damages. Significant changes of the constant component ${f}_{{dc}}$ and the double-fundamental harmonic component $\text{2}{f}_{s}$ observed in these signals’ spectra under the stator-winding failure become the basis for the failure diagnostics in various operating conditions of the DFOC IM drive. The superiority of the proposed detection algorithm over the classical motor current signature analysis (MCSA) method is described. The online monitoring process is shown. Simulation and experimental results are presented to validate the proposed detection algorithm.

Detection of stator incipient faults and identification of faulty phase in three‐phase induction motor – simulation and experimental verification

Motor current signature analysis is a well-known method for the diagnosis of stator incipient faults on a three-phase induction motor (IM). In classical motor current signature analysis the fault feature is extracted by analysing the frequency spectrum obtained from the Fourier analysis. However, for proper fault diagnosis, time–frequency domain analysis is required. This study proposes an algorithm based on wavelet analysis for detection of stator incipient faults and identification of faulty phase in three-phase IM. A turn level distributed parameter model of a 3-hp IM is considered for the simulation of inter-turn faults. The parameters used in the simulated model are calculated by conducting experiments on a 3-hp IM. This model is validated by comparing the frequency response of the simulated model with the frequency response measured on practical machine. The proposed algorithm uses an adaptive threshold-based logic for detecting the inter-turn faults and identifying the faulty phase. The algorithm is validated with data generated by the specially designed 3-hp IM. The experimental and simulation results show that the proposed algorithm is effective in detecting the inter-turn faults and identifying the faulty phase even in the presence of supply unbalance conditions.

Transient-Based Rotor Cage Assessment in Induction Motors Operating With Soft Starters

The reliable assessment of the rotor condition in induction motors is a matter of increasing concern in the industry. Although rotor damages are more likely in line-started motors operating under high inertias, some cases of broken rotor bars in motors supplied via soft starters have been also reported in the industry. Motor current signature analysis (MCSA) is the most widely spread approach to diagnose such failures. However, its serious drawbacks in many real industrial applications have encouraged investigation on alternative methods enhancing the reliability of the diagnosis. This paper extends a recently introduced diagnosis methodology relying on the startup current analysis to the case of soft-starter-operated motors. The approach has proven to provide very satisfactory results, even in cases where the classical MCSA does not lead to correct diagnosis conclusions. However, its extension to operation under soft starters was still a pending issue. The experimental results shown in this paper ratify the validity of the proposed diagnosis approach in soft-starter-operated induction motors.

An improved spectral analysis of the stray flux component for the detection of air-gap irregularities in squirrel cage motors

For machines׳ monitoring purpose, the classical motor current signature analysis has shown its weakness in distinguishing the eccentricity occurrence in presence of others mechanical faults. Although Park׳s vector approach can cover this drawback, the high cost due to the requirement to use three current sensors associated with an advanced processing technique, makes it less desired by industrialists. In this paper, we suggest an alternative diagnosis method based on a suitable processing of the stray flux data. The experimental results have revealed the potential of a simple search coil for the detection and the distinction of the accurate eccentricity nature even in presence of similar mechanical faults.

Detection and estimation of demagnetization faults in permanent magnet synchronous motors

This paper presents a symbolic dynamic method for health monitoring of permanent magnet synchronous motors (PMSMs), which involves abstraction of a qualitative description from a dynamical system representation of the PMSM. The underlying algorithms rely on state-space embedding of the PMSM's output line current and discretization of the resultant pseudo-state and input spaces. System identification is achieved through inference of the PMSM's dynamical system behavior, and the deviation of the system's output behavior from the nominal expected behavior yields a measure of the estimated fault. A special-purpose test bed has been designed and fabricated for experimental validation of the health monitoring algorithm via controlled accelerated deterioration of magnetization in the PMSM. The performance of the proposed algorithm has been compared with that of a classical motor current signature analysis (MCSA) procedure as well as with a benchmark particle filter for fault detection in PMSMs.

Motor square current signature analysis for induction motor rotor diagnosis

Induction machines play an important role in today’s industry. Thus, preventive maintenance combined with fault diagnosis techniques have become an essential issue. One of the most used techniques for the diagnosis of faults in the induction machine is motor current signature analysis (MCSA). This approach presents some limitations for induction motor rotor diagnosis, particularly for small faults. In this paper, a new motor square current signature analysis (MSCSA) fault diagnosis methodology is presented. The proposed technique is based on three main steps: first, the induction motor current is measured; secondly, the square of the current is computed; and finally a frequency analysis of the square current is performed. This technique allows more information to be obtained from a motor with a rotor fault than the classical MCSA approach. Simulation and experimental results are presented in order to confirm the theoretical assumptions. This methodology has also been tested for the identification of two distinct faults (broken bars and rotor eccentricity).