Detection and Diagnosis of Compound Faults in a Reciprocating Compressor based on Motor Current Signatures

Induction motors are the most common driver in the industry and consume tremendous energy every year. Monitoring the status of a motor and its downstream equipment and diagnosing faults in time not only avoids great damage to mechanical systems but also allows the motor to run at optimal efficiency. This paper studies the use of information from motor current signals to detect and diagnose faults of a reciprocating compressor (RC) and its upstream three-phase motor. The motor is applied by the RC with an oscillator torque which induces additional components in measured current signals. Moreover, the current signatures contain changes with the torque profiles due to different types of faults. Based on these analytical studies, experimental studies were carried out for different common RC faults, such as valve leakage, intercooler leakage, stator asymmetries and the compounds of them. The envelope analysis of current signals allows accurate demodulation of the torque profiles and thereby it can be combined with overall current levels for implementing model based detections and diagnosis. The results show these simulated faults can be separated under all operating pressures

An accurate inter-turn short circuit faults model dedicated to induction motors

Safety, disponibility and continuity of industrial systems are major issue in maintenance. In the last decades, these points are the important axes in the field of research. In fact, in many industrial processes research has picked up a fervent place and a particular importance in the area of fault diagnosis of electrical machines, in fact, a fault prognosis has become almost indispensable. The need of a mathematical model of three-phase induction machine, suitable for the simulation of machines behaviour under fault conditions, has received considerable attention. The paper presents a new practical and more precise model for induction motors after introducing inter turn short circuits faults. The proposed model is based on coupled magnetic circuit theory, capable to take into account any electrical asymmetry conditions. To verify the exactitude and the effectiveness of the model, simulation results for induction machine under interturn short circuit fault are presented. In spite of its simplicity, the proposed model is able to provide useful indications for diagnostic purposes. Experimental study is presented at the end of the paper to show that the proposed model predicts the induction machine behavior with a good accuracy

Diagnosis of Compound Faults in Reciprocating Compressors Based on Modulation Signal Bispectrum of Current Signals

This paper studies induction motor current signatures to detect and di-agnose faults of a two-stage reciprocating compressor (RC) which creates a varying load to the motor. It also examines the influences of stator winding faults on differ-ent common faults of the compressor. Both the conventional spectrum analysis and the state of the art modulation signal bispectrum (MSB) analysis are used to process the current signals for attaining an accurate characterisation of the modulation in-duced by the variable loads and thereby developing reliable diagnostic features. The experimental studies examine different RC faults including valve leakage, inter-cooler leakage, stator asymmetries and their compounds. The results demonstrated that the MSB has a better performance in differentiating spectrum amplitudes caused by different faults especially the compound fault. Thus the MSB based fea-tures are demonstrated to be more reliable and accurate as the analysis techniques for motor current based diagnostics

Evaluation of machine learning techniques for electro-mechanical system diagnosis

The application of intelligent algorithms, in electro-mechanical diagnosis systems, is increasing in order to reach high Reliability and performance ratios in critical and complex scenarios. In this context, different multidimensional intelligent diagnosis systems, based on different machine learning techniques, are presented and evaluated in an electro-mechanical actuator diagnosis scheme. The used diagnosis methodology includes the acquisition of different physical magnitudes from the system, such as machine vibrations and stator currents, to enhance the monitoring capabilities. The features calculation process is based on statistical time and frequency domains features, as well as timefrequency fault indicators. A features reduction stage is, additionally, included to compress the descriptive fault information in a reduced feature set. After, different classification algorithms such as Support Vector Machines, Neural Network, k-Nearest Neighbors and Classification Trees are implemented. Classification ratios over inputs corresponding to previously learnt classes, and generalization capabilities with inputs corresponding to learnt classes slightly modified are evaluated in an experimental test bench to analyze the suitability of each algorithm for this kind of application.Peer ReviewedPostprint (author’s final draft

Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal operating voltages and currents, without relying on complex motor modeling or internal performance parameters not readily available

EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals

Fault detection in VSD-fed induction motors through Park’s impedance and fuzzy systems

Industrial applications that require speed control have been increasing in recent years and the use of variable speed drives (VSD) for feeding induction motors (IM) is more common. Therefore, methodologies for detecting faults on VSD-fed IM are needed with the aim of minimize cost in maintenance and reduce the power consumption. In this work a methodology for fault diagnosis is proposed through spectral patterns obtained from the Park’s impedance. Broken rotor bar, unbalanced mass, and misalignment conditions are investigated and a fuzzy-logic diagnosis system is proposed for asserting the VSD-fed IM condition. Results show high effectiveness in detection of the investigated fault conditions through the proposed methodology, which has been validated with experimental tests.Index terms: fault detection, impedance analysis, induction motors, park transform, variable speed drives

Chromatic monitoring of gear mechanical degradation based on acoustic emission

This paper presents a methodology for the feature estimation of a new fault indicator focused on detecting gear mechanical degradation under different operating conditions. Preprocessing of acoustic emission signal is performed by applying chromatic transformation to highlight characteristic patterns of the mechanical degradation. In this study, chromaticity based on the computation of the hue, light, and saturation transformation of the main acoustic emission intrinsic mode functions is performed. Then, a topology preservation approach is carried out to describe the chromatic signature of the healthy gear condition. Thus, the detection index can be estimated. It must be noted that the applied chromatic monitoring process only requires the characterization of the healthy gear condition, being applicable to a wide range of operating conditions of the gear. Performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for monitoring gear mechanical degradation in industrial applications.Peer ReviewedPostprint (published version

The use of a Multi-label Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults based on the Start-up Transient

[EN] In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three "problem transformation" methods are tested and compared. For the feature extraction stage, the startup current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multi-label framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.This work was supported in part by the Spanish MINECO and FEDER program in the framework of the "Proyectos I + D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia" under Grant DPI2014-52842-P and in part by the Horizon 2020 Framework program DISIRE under the Grant Agreement 636834.Georgoulas, G.; Climente Alarcón, V.; Antonino-Daviu, J.; Tsoumas, IP.; Stylios, CD.; Arkkio, A.; Nikolakopoulos, G. (2016). The use of a Multi-label Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults based on the Start-up Transient. IEEE Transactions on Industrial Informatics. 13(2):625-634. https://doi.org/10.1109/TII.2016.2637169S62563413

The h-EXIN CCA for Bearing Fault Diagnosis

This paper presents the hierarchical EXIN CCA, which represents a novel and reliable approach to complex pattern recognition problems. The methodology is based on the EXIN CCA, which is an extension of the Curvilinear Component Analysis, for data reduction, and neural networks for data classification. The effectiveness of this condition monitoring scheme is verified in a demanding bearing fault diagnostic scenario