Electrical Signature Analysis Research Articles

Electrical Signature Analysis for Open-Circuit Faults Detection of Inverter With Various Disturbances in Distribution Grid

This paper proposes an electrical signature analysis method for open-circuit faults (OCFs) detection of inverter with various disturbances in distribution grid. According to the fault mechanism, the fundamental value, rated harmonics and direct current component of three-phase currents are used as fault electrical signatures. The signatures are estimated by unscented Kalman filter (UKF) and recognized by extreme learning machine (ELM) for fault detection. Both OCF of single switch and OCFs of multiple switches are tested with consideration of direct disturbances such as load change, overcurrent and bias current, and indirect disturbances such as grid frequency variation, background harmonics and unbalanced voltage dip. The simulations and experiments show that the OCF detection of the new method is still accurate even with these disturbances, and reveal that only the signatures of the faulty phase current are immune to the disturbances while the ones of unfaulty phases are not. The robustness when facing the various disturbances and all explainable detection results make the new method suitable and effective for OCF of inverter detection in complicated distribution grid environment.

Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: a data-driven approach

Abstract Recently, prognostic and health management (PHM) has become a prominent field in modern industry. The rotate vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The RV reducer is known for its unique characteristics of small size, efficient speed transmission, and high torsion. The RV reducer is prone to several kinds of faults, due to its continuous operation in an industrial robot. To keep the operation smooth and steady, timely PHM of the RV reducer has become essential. Previously, the RV reducer fault was diagnosed via various techniques, such as ferrography analysis, vibration analysis, and acoustic emission analysis. However, these conventional techniques have various issues. To resolve those issues, we introduce a novel approach to use the embedded electrical current system for the fault detection of the RV reducer. However, this is quite complicated to investigate mechanical fault using an electrical current signature, since the RV reducer is not an integral part of the electric motor, and finding a fault pattern in faulty components needs thorough examination. We therefore focus on the application of machine learning (ML) for fault classifications. We present an approach for feature extraction, feature selection, and feature reduction using the information obtained from the motor current signature analysis to create an ML-based fault classification system with distinguishable prominent features. Finally, the authenticity of the presented approach is justified via the improved values of evaluating parameters, such as accuracy, specificity, and sensitivity, for ML classifiers.

An Autonomous Electrical Signature Analysis-Based Method for Faults Monitoring in Industrial Motors

Rotating machines are widely used in industries, manufacturing, and oil and gas plants as a critical component for process availability. The inadvertent failure of these rotating machines causes significant process downtime and incurs higher repair costs and loss of revenue. These failures can belong to electrical, thermal, or mechanical fault categories. Early detection of all these faults is very much critical for avoiding complete failure of these machines and requires continuous 24/7 online monitoring using sensors or intelligent electronic devices (IEDs). However, an affordable low-cost and efficient online monitoring is desired to practice specifically for medium-voltage (MV) machines which have a larger install base. Electrical signature analysis (ESA) technology offers such flexibility and requires measuring just current and/or voltage at a motor control panel for machine health diagnosis, unlike vibration analysis (VA) requiring installation of sensors and its wiring on the machine. Furthermore, an intelligent, self-reliable, and autonomous ESA procedure is required for monitoring the machines due to the lack of ESA standards in practice unlike for VA. This article proposes a new autonomous electrical signature analysis (AESA)-based measurement technique implemented in IED, that is, Protection Relay, offering 24/7 online monitoring. The proposed technique implemented in Relay not only avoids dependence on standalone ESA hardware but also provides earlier detection of failures using peak and energy magnitudes computation approach at fault frequencies. To validate the proposed method, various tests were performed on the actual 1000- and 300-HP motors with/without mechanical faults in a machine repair shop and the results are discussed. The performance of the proposed method is also compared with the commercially available third-party ESA device results proving the efficacy.

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

A NOVEL TECHNIQUE TO MONITOR MEDICAL EQUIPMENT MODES OF OPERATION

Quantitative measurement of the effective usability of the medical equipment is an important parameter in the quality and performance assessment. This paper introduces a simple non-invasive technique for real time monitoring of medical equipment modes of operation based on its power consumption pattern. Mode of operation detection is needed to specify the medical equipment reliability, availability, maintainability and usability. The Electrical Signature Analysis technique (ESA), is applied to monitor the overall power consumption of the electromechanical and power components inside the medical equipment. ESA is extracted using the Root Mean Square (RMS) of the electric current measured by a meter interfaced to a PC via its USB port. The ESA of the medial equipment is recorded, analyzed and correlated with the stored electric current consumption patterns of each specific mode of operation. The results were promising to accomplish the medical equipment monitoring application.

Diagnostics of inter-turn short-circuit in the stator winding of the induction motor

A stator malfunction occurs due to insulation damage, which means that the stator is directly connected to the power supply, and a direct connection is the direct cause of a major accident. For this reason, a lot of research is being done to identify faults. In recent years, studies on Motor Electrical Signature Analysis are being performed, sensorless methods for fault diagnosis of the stator of an induction motor. However, it has problems with it, it has the difficulty in interpreting multi-faults and in detecting an inter-turn short circuit of a stator. This paper suggested a tool that can diagnose 2-turn short circuit, through the relationship between self-inductance and mutual inductance in the synchronous reference frame of a D-Q transformation.

A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems

The condition of synchronous generators (SGs) is a matter of great attention, because they can be seen as equipment and also as fundamental elements of power systems. Thus, there is a growing interest in new technologies to improve SG protection and maintenance schemes. In this context, electrical signature analysis (ESA) is a non-invasive technique that has been increasingly applied to the predictive maintenance of rotating electrical machines. However, in general, the works applying ESA to SGs are focused on isolated machines. Thus, this paper presents a study on the condition monitoring of SGs in bulk electric systems by using ESA. The main contribution of this work is the practical results of ESA for fault detection in in-service SGs interconnected to a power system. Two types of faults were detected in an SG at a Brazilian hydroelectric power plant by using ESA, including stator electrical unbalance and mechanical misalignment. This paper also addresses peculiarities in the ESA of wound rotor SGs, including recommendations for signal analysis, how to discriminate rotor faults on fault patterns, and the particularities of two-pole SGs.

Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems

Abstract Over the last decade, research into photovoltaic (PV) technology has shifted from a race for the highest efficiency to the increase of the performance reliability in the field. A major part of current research activities focuses on the reliability of the installations and the guaranteed lifetime output through constant, solid and traceable PV plant monitoring. In this domain, several PV monitoring strategies for the early diagnosis of failures in grid-connected PV systems have been proposed in the literature and this study seeks to provide an overview of all the data analytic methods used by the research community and industry for the detection and classification of failures from acquired performance data of grid-connected PV systems. Insight into the performance monitoring requirements (parameters and resolution) for the detection of failures in monitored PV systems, as well as the various techniques used for their classification is also provided. Finally, this overview covers the data analytic methods based on electrical signature, numerical and statistical analysis and are summarised according to the type of failure, input requirements and validation procedure.

FPGA based on-line fault diagnostic of induction motors using electrical signature analysis

Preventive maintenance is one of the main concerns in modern industry, in which early failure detection increases the lifecycle of machines. In this paper, electrical signature analysis is employed to indicate the development or existence of faults within the proposed system and this is achieved by embedding a real-time frequency analysis of the motor current. The term in the title electrical signature analysis basically refers to the motor current or voltage attributes are being used as a transducers to detect the changes in their spectrum in both the conditions; healthy and unhealthy. The algorithm used for analyzing the signals in frequency domain is done using Fast Fourier transform. In this work, we have focused on failure of bearing part of single phase induction motor and developed hardware for monitoring conditions (i.e. health of the motor) in run time. Because of the simplicity of this technique the mechanism of fault diagnosis is employed using an FPGA approach that offers re-configurability. This work can be very useful in industrial setup where there are 100 motors working together for some production lines. The findings show promising results which could lead to better reliability performance of the induction motor and lower maintenance costs.

Electrical Signature Analysis-Based Detection of External Bearing Faults in Electromechanical Drivetrains

An electrical signature analysis (ESA)-based method to detect bearing defects in electromechanical drivetrains (e.g., wind turbines) is proposed in this paper. The novelty of the proposed technique is justified by its capability to detect a defect that occurs in a drivetrain bearing, external to the machine. Unlike the traditional vibration-based method, the electrical measurements of the terminal machine are utilized in this case to perform the fault detection. The proposed ESA-based method is developed utilizing a new electrical signal model of bearing defect, proposed in this paper, based on the excitation of torsional resonances. The proposed ESA-based method and the signal model are validated through experimental tests. The proposed technique demonstrates excellent fault detection capability even at low operating speed, which is advantageous over the traditional vibration-based method in terms of sensor cost, installation complexity, and detection reliability.

Discrimination of Synchronous Machines Rotor Faults in Electrical Signature Analysis Based on Symmetrical Components

Electrical signature analysis (ESA) has been successfully applied to predictive maintenance of synchronous machines. The fault diagnosis is performed by analyzing failure patterns in the current or voltage spectra, which allow the discrimination of a healthy and a faulty condition on the monitored machine. Generally, the failure patterns are a function of the line frequency, rotor rotation frequency, and some structure features of the machine. The rotor rotation frequency pattern, for instance, is indicative of rotor mechanical problems and rotor winding interturn short-circuit. An increase of this frequency component magnitude may indicate the incipience of a rotor fault but may not discriminate the nature of the fault (electrical or mechanical). Thus, it is necessary to distinguish the effects of electrical and mechanical faults in these components in order to get a reliable diagnosis of the machine. No works have been found in the literature approaching this specific issue. This paper proposes a simple and innovative methodology to distinguish the effect of electrical and mechanical faults in the mentioned ESA failure patterns based on the method of symmetrical components. Its effectiveness is validated by experiments performed on a synchronous generator test rig. The proposed condition monitoring system is simple, low cost, and low intrusive, as it only relies on stator electrical quantities. Moreover, it is in operation in a Brazilian 404-MW thermal power station.

Detection and modelling of incipient failures in internal combustion engine driven generators using Electrical Signature Analysis

Condition-based maintenance of electric generators have been gaining increasing importance due to the electricity demand and the criticality that this equipment represents to electrical power systems. In this context, this paper proposes a methodology and a system for detection and modeling of incipient failures in the components of internal combustion engine-driven generators based on Electrical Signature Analysis (ESA). The proposed methodology enables the detection of incipient faults both in the prime mover and in the coupled synchronous generator, only relying on measurements of the generator stator voltages and currents. The proposed ESA failure patterns are based on defined frequencies and the structural features of the machine, so they can be reproduced in a wide range of engine-generators sets. The main advantages of the proposed system are its low intrusiveness, feasible installation and cost efficiency. A scale model laboratory has been designed to simulate faults in a small diesel generator and apply the ESA methodology to detect these faults and obtain the failure patterns. Experimental results are presented to prove the effectiveness of the proposed methodology. The main results include the findings that exciter generator unbalance induces electrical unbalance components, exciter diode short circuit induces even harmonics, intake valve failure and piston ring failure induce multiples of rotation frequency components, and mechanical misalignment of the engine generator set induces multiples of half order speed frequency components on ESA. Moreover, the proposed prototype is installed at two large in-service internal combustion engine-driven generators and examples of signal analysis are provided.

Analysis of Combined Motor Current Signature and Vibration-Monitoring Techniques in the Study of Broken Bars in Three-Phase High-Performance Induction Motors

Anomalies in operation of the motor modify harmonic content of motor supply current. Motor electrical current signature analysis (MCSA) is sensing an electrical signal containing current components. In this work, by introducing progressive failures to a healthy motor, we investigated the efficacy of combining motor current signature analysis with vibration-monitoring techniques for the detection of broken bars in the squirrel-cage rotors of three-phase high-efficiency induction motors, which are the new standard in Brazilian industry. Our results demonstrate that the combined approach improves reliability in the diagnosis of broken bars.

Online condition monitoring of wind turbines through three-phase electrical signature analysis

In the context of the KAStrion European project, a complete solution was proposed in order to monitor wind turbines. The developed solution comprises both hardware and software parts of the condition monitoring system. In terms of software, modules for vibration analysis and electrical signal analysis have been developed. The current paper presents the electrical analysis solution proposed in the context of this project. The electrical module is able to detect both mechanical and electrical faults in a wind turbine system. The goal of the mechanical fault detection using electrical signals is to confirm the faults also detected by vibration analysis, while the main focus of the module remains the detection of electrical faults. Results showing the performance of mechanical fault detection are presented using electrical signals acquired on the test-bench developed for testing the KAStrion system. Moreover, results regarding the electrical unbalance are presented using signals acquired on a three-phase transformer. The final solution has been implemented on two onshore wind turbines since the end of 2014, and online condition monitoring results are presented at the end of the paper.

Electrical Signature Analysis Performed on Vertical Turbine Pump Motors in Municipal Application

Electrical Signature Analysis Performed on Vertical Turbine Pump Motors in Municipal Application

Detection of Gearbox Bearing Defects Using Electrical Signature Analysis for Doubly Fed Wind Generators

Drivetrain failures may cause severe damage to wind turbines. In the previous work, detection of failures in generator bearing and gearbox gears using electrical signature analysis (ESA) has been investigated. However, the detection of defects of bearings in the gearboxes has been a major gap. Bearing defects in gearboxes are believed to be one of the root causes of wind drivetrain failures. In this paper, a novel ESA-based monitoring technique is proposed for monitoring gearbox bearing defects in wind turbines, which is the first ESA technique reported that is capable of detecting bearing defects in gearboxes. A novel electrical signature tool, i.e., electrical multiphase imbalance separation technique, has been used to improve the signal-to-noise ratio in ESA. The principle of gearbox bearing defect detection is presented in detail. The proposed approach is validated by experimental results obtained from a 25-hp wind drivetrain simulator, which is designed to simulate 1.5-MW wind turbines as well as in the field on 1.5-MW wind turbines. The experimental results show that the proposed approach is capable of providing accurate detection of gearbox bearing failures at an early stage. The proposed approach is cost-effective, with reliable detection of defects compared to existing techniques.

(Invited) (ECS Electronics & Photonics Division Award )Wafer-Level Step-Stressing of InGaP/GaAs HBTs

Since InGaP/GaAs heterojunction bipolar transistors (HBTs) are utilized in a wide variety of RF and other applications, a great deal has been learned about their reliability. Nevertheless, this knowledge is limited by the fact that most reliability studies heavily emphasize the stress-related evolution of a single parameter, the DC current gain, beta. We have found that interrupted stressing experiments, with complete characterization of HBTs during the interruption, can give a more complete picture of the degradation that occurs during bias stressing of HBTs. We have previously correlated electrical signatures with degradation in step-stressing of packaged InGaP/GaAs HBTs [1]. In this work we will present some results of step-stress experiments which have been conducted at the wafer level for InGaP/GaAs HBTs. Wafer-level evaluation is advantageous due to the potential for greater automation and flexibility in the testing as well as the ability to identify problems at an earlier stage in the process.The HBTs are stressed at room temperature at a constant collector voltage. Stressing is initiated at a given base current, held for a fixed time, which is typically 10 s. The applied stress is stepped up to a higher level of base current and the process is iterated until a failure is recorded. Full DC parametric electrical characterization of the HBT gets carried out initially and after certain predefined stress steps. Although the stress is typically carried near room temperature, it is understood that the junction of the HBT is effectively self-heated and can reach extremely high temperatures.A certain set of degradation processes are responsible for stress-induced failures and these occur singly or in combinations. In addition, many HBTs within a wafer and among duplicate wafers have not only a number of common failure modes, but a tendency towards a consistently realized destructive power limit. The consistency of these results and the utility of electrical signature analysis lead us to conclude that wafer-level step-stressing is an excellent tool for quickly monitoring the HBT quality. We have also found many commonalities in the wafer-level and package level results for the ceramic packages used in a previous study [1].The degradation of HBTs by these stressing methods leads eventually to a large drop in current gain. Thus, the interrupted step stressing method arrives at the same end state as continuous, single-parameter monitoring, but with much more clarity about the degradation processes that occur on the way to the end state. The interrupted step-stress process in combination with electrical signature analysis is very likely to find the weak link in an HBT process. We will discuss the utility of the technique in terms of the opportunities to ruggedize the process. It is also possible that the identified weak link is fundamental to the technology or inconsequential in terms of meeting reliability requirements. In that case, wafer-level step-stressing is still an excellent tool for early identification of quality issues that may affect reliability through monitoring the consistency (or lack thereof) in degradation observed across different wafers over different periods of time. A.G. Baca, A.J. Scruggs, A. Gorenz, T.R. Fortune, J.F. Klem, R.D. Briggs, J.B. Clevenger, G.A. Patrizi, and C.T. Sullivan, “A Survey of Electrical Signatures Characteristic of Step-Stressed InGaP/GaAs HBTS,” ECS Transactions vol. 50 (6), pp. 273-282 (2012). Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

Detection of Induction Motors Rotor/Stator Faults Using Electrical Signatures Analysis

In this work induction motor faults detection using electrical signature analysis techniques are introduced, and the advantages and disadvantages of these techniques are explained. Stator phase current signature, stator current locus diagram, stator current vector signature, partial power signature, and total power signature are selected for the simulation study applied for 4Kw squirrel cage induction motor. Simulation results are shown to explain how these signatures can be used to detect rotor/stator faults. And finally the performance of each technique based on the sensitivity to fault severity, the effect of motor loading, and cost required for the practical implementation is discussed.

Advanced Electrical Signature Analysis to Track the Health of Aircraft Electrical Generators

Advanced Electrical Signature Analysis to Track the Health of Aircraft Electrical Generators

Agile diagnostic tool based on electrical signature analysis

AbstractToday, time based maintenance is the most common strategy, to assure a certain level of productivity and reliability in the industrial plants, with a fixed interval length between two maintenance actions. However, given the stochastic nature of the faults, plant equipments and machines usually fail unexpectedly, before the scheduled maintenance action. Therefore, condition based maintenance (CBM) has been introduced, without any fixed interval length between the maintenance actions, as a more efficient maintenance policy. A technique developed for condition based maintenance of electro-mechanical systems is electrical signature analysis. The basic idea behind electrical signature analysis is that, time-dependent load and speed variations in an electro-mechanical system generally induce correlated small variations in the system's electrical response. Electrical signature analysis analyzes these small variations providing diagnostic information. A tool based on this technique is agile in its adoption since no specific sensors must be installed on the machine, but only power signal must be measured and thus the implementation of such tool can be considered simple and flexible. This paper presents a tool that has such kind of characteristics. The tool processes power signals acquired by transducers installed on the electrical lines of electro-mechanical systems, for machine fault diagnosis. The data acquired are processed through some steps, using some signal indicators developed to understand the machine behaviour.