Ioannis P. Tsoumas

Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stators three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stators current independently of the motors load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.

Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines

This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology.

A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors

One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.

Influence of the Inverter's Modulation Technique on the Audible Noise of Electric Motors

Noise emissions of inverter-driven electric motors are highly influenced by additional electromagnetic noise generated by the current harmonics from inverter operation. The amplitude and the frequency of these harmonics depend on the modulation technique applied in the power electronic converter. The purpose of this paper is to highlight the important role that modulation techniques play in noise generation in variable-speed drives and to analyze the different characteristics of current harmonics and generated noise. A good understanding of the available choices and their characteristics, advantages, and limitations is necessary in order to optimize the acoustic design of the “motor-converter” system.

Case Studies of Acoustic Noise Emission From Inverter-Fed Asynchronous Machines

The issue of noise emission from electric drives is becoming increasingly important. Motor manufacturers have to comply with certain standards in order to assure the high competitiveness of their products. At the same time, with todays variable speed drives, which are supplied with nonsinusoidal voltages, the issue of noise reduction has become more complex. This is because the influence of additional factors, compared to machines supplied with sinusoidal voltage, must be considered over a wide speed range. The key to optimizing the machines acoustic behavior is the thorough knowledge of the influence of the different noise sources and the excitation mechanisms over the complete speed range. Apart from the theoretical analysis and the simulation, an experimental investigation is necessary to obtain a better understanding of the previously mentioned factors and to minimize the machines acoustic noise. This paper presents some characteristic case studies of acoustic noise emission in asynchronous machines supplied from voltage source inverters in order to examine the influence of diverse factors on the total noise level.

Multi-harmonic tracking for diagnosis of rotor asymmetries in wound rotor induction motors

Most of the research work hitherto carried out in the induction motors fault diagnosis area has been focused on squirrel-cage motors in spite of the fact that wound-rotor motors are typically less robust, having a more delicate maintenance. Over recent years, wound-rotor machines have drawn an increasing attention in the fault diagnosis community due to the advent of wind power technologies for electricity generation and the widely spread use of its generator variant, the Doubly-Fed Induction Generators (DFIGs) in that specific context. Nonetheless, there is still a lack of reliable techniques suited and properly validated in wound-rotor industrial induction motors. This paper proposes an integral methodology to diagnose rotor asymmetries in wound-rotor motors with high reliability. It is based on a twofold approach; the Empirical Mode Decomposition (EMD) method is employed to track the low-frequency fault-related components, while the Wigner-Ville Distribution (WVD) is used for detecting the high-frequency failure harmonics during a startup. Experimental results with real wound-rotor motors demonstrate that the combination of both perspectives enables to correctly diagnose the failure with higher reliability than alternative techniques relying on a unique informational source.

An automated thermographic image segmentation method for induction motor fault diagnosis

Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

Automatizing the broken bar detection process via short time Fourier transform and two-dimensional piecewise aggregate approximation representation

This work presents an automated approach for detecting broken rotor bars in induction machines using the stator current during startup operation. The currents are analyzed using the well-known Short Time Fourier Transform (STFT) producing a two-dimensional time-frequency representation. This representation contains information regarding the presence of a characteristic transient component but requires further processing before it can be fed into a standard classification algorithm. In this work, this part is performed using the two dimensional extension of Piecewise Aggregate Approximation (PAA) that can deal with the two dimensional representation of STFT. The results (with both simulated and experimental data) suggest that the method can be used for the automatic detection of broken bars and even for determining the fault severity. Moreover, its low computational burden makes it ideal for its future use in online, unsupervised systems, as well as in portable condition monitoring devices.

The European Standard EN 50598-2: Efficiency classes of converters and drive systems

A new Standard that defines, for the first time, efficiency classes for converters and electric drive systems will take effect in 2014. This paper focuses on the drive system, which is referred to as Power Drive System (PDS) in the Standard. The paper also addresses the procedure for determining the drive system efficiency classes, referred to as IES classes, as well as the possible loss determination methods - such as measurement, calculation, etc. Finally, the authors provide an example of the losses distribution in a typical PDS and highlight the key factors that should be considered when undertaking a system optimization.

Probability model for discharge activities in bearings of converter-fed electric motors

Voltage source inverters in electrical drive systems are the main sources of possible mechanical degeneration inside the anti-friction bearings of downstream drive train components. In order to find the right countermeasures to avoid these parasitic effects, knowledge of the switching conditions of the converter and the subsequent effects are an important issue. This paper focuses on the converters inherent common-mode voltage and its impact on the mechanical drive train elements. Using an electrical bearing model, a method will be presented and discussed to approximate the probability for discharges in the bearings of converter-fed electric motors.