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![Figure 2 Current spectrum of induction motor, [20]](profile/Dubravko-Miljkovic/publication/304094187/figure/fig2/AS:391122015801345@1470262163925/Current-spectrum-of-induction-motor-20_Q320.jpg)
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![Figure 5 Air gap- dynamic eccentricity, [11]](profile/Dubravko-Miljkovic/publication/304094187/figure/fig5/AS:391122787553290@1470262347493/Air-gap-dynamic-eccentricity-11_Q320.jpg)
Motor electrical current signature analysis (MCSA) is sensing an electrical signal containing current components that are direct by-product of unique rotating flux components. Anomalies in operation of the motor modify harmonic content of motor supply current. This paper presents brief introductory review of the method including fundamentals, fault...
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Pumps have a wide range of applications. Methods for fault detection of motors are increasingly being used for pumps. In the context of this paper, a test bench is built to investigate circulation pumps for faults. As a use case, the fault of impeller clogging was first measured and then examined with the help of motor current signature analysis. I...
Citations
... The working strategy of this model is collection and processing the motor signatures and comparing these signatures in healthy models with faulty models. Of course, these signatures can be current, vibration, noise, heat, etc [6]. Usually, vibration signatures are used in mechanical bearing fault diagnosing [7]. ...
... Meanwhile, vibration sensors are susceptible to environmental disturbances, which could reduce the measurement accuracy and lead to misdiagnosis [9]. In the 1970s, the concept of motor current signature analysis (MCSA) was first proposed to monitor inaccessible motors placed in nuclear power plants [10]. In this technique, the stator current characteristics directly linked to the instantaneous change of rotating flux caused by mechanical or electrical faults can be used for fault detection. ...
Centrifugal pumps are essential in many industrial processes. An accurate operation diagnosis of centrifugal pumps is crucial to ensure their reliable operation and extend their useful life. In real industry applications, many centrifugal pumps lack flowmeters and accurate pressure sensors, and therefore, it is not possible to determine whether the pump is operating near its best efficiency point (BEP). This paper investigates the detection of off-design operation and cavitation for centrifugal pumps with accelerometers and current sensors. To this end, a centrifugal pump was tested under off-design conditions and various levels of cavitation. A three-axis accelerometer and three Hall-effect current sensors were used to collect vibration and stator current signals simultaneously under each state. Both kinds of signals were evaluated for their effectiveness in operation diagnosis. Signal processing methods, including wavelet threshold function, variational mode decomposition (VMD), Park vector modulus transformation, and a marginal spectrum were introduced for feature extraction. Seven families of machine learning-based classification algorithms were evaluated for their performance when used for off-design and cavitation identification. The obtained results, using both types of signals, prove the effectiveness of both approaches and the advantages of combining them in achieving the most reliable operation diagnosis results for centrifugal pumps.
... The most common method for FDD of motors in the nuclear industry is motor current signature analysis, where the changes in the properties of the current signals are used to detect faults. [33][34][35] These property changes in the current signal are more likely to be manifested in torque, possibly explaining the good torque performance as a variable. ...
Control rods and elements manage the power distribution in nuclear reactors through the motion of banks of rods distributed throughout the core. These positional changes are achieved through the actuation of fine motion control rod drive (FMCRD) mechanisms. In the BWRX-300 design by GE-Hitachi, this mechanism is electrically driven by a servomotor that allows for high-precision control of power outputs. Under operational transients, such as load-following, accurate and precise operation of these servomotors is necessary over long periods of time, so they are key maintenance targets to maintain availability and operational flexibility. Swiftly and precisely identifying faults in the drive mechanisms will support predictive maintenance and reduced costs. This paper used three different types of simulated faults to test the fault detectability of principal component analysis (PCA) when considering the simulated operations of banks of control rods and their associated servomotors. These faults were stator short-circuit faults, ball screw jam faults, and ball screw wear faults. Torque and position were monitored in the simulation. The position signal was insufficient to detect mechanical faults. Torque signals for each servomotor in the bank of rods undergoing multiple position demand changes were projected to a reduced dimensional space via PCA. Q and T 2 statistics were employed for anomaly detection. Using this approach, all faults were detected, and the anomalies were isolated to the faulty FMCRD mechanism
... Motor current signature analysis (MCSA) is an established condition-monitoring method based on monitoring distinct electric current patterns of the stator in the induction motor [2], which drives the pump, using a combination of signal processing, statistical techniques and machine learning [3]. The MCSA-based systems can be deployed by simply attaching current clamps to power supply wires, which can be done away from the pump itself. ...
The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.
... The different fault detection techniques in MCSA include Fast Fourier transform, demodulation, wavelet transform and parks vector approach. Fast Fourier Transform is a popular method, but it has a resolution problem due to insufficient data, [26]. The wavelet transform originated to overcome the drawbacks of the short time Fourier transform (SFT) [28,29] and Wigner Ville distribution (WVD) [30,31]. ...
This book series aims to bring together researchers and practitioners from academia and industry to focus on recent systems and techniques in the broad field of electronics, instrumentation and communication Engineering. Original research papers, state-of-the-art reviews are invited for publication in all areas of Electronics & Instrumentation Engineering.
... The faults occur in the mechanical driving system or in the induction motor. The faults in mechanical system are owing to the irregularities in the air-gap, failures in the gearbox or driving system, defect in shaft [2]. The motor faults can be classified based on the fault on the stator side or rotor side. ...
There are various methods for the condition monitoring and this paper focuses on the multi resolution current signature analysis for fault prediction of induction motors. Variable frequency drives-based induction motors are used widely in industries. Monitoring the health of the motors is of great importance to reduce downtime and increase productivity. The multi resolution coefficients features from current signal are extracted using empirical wavelet transform. The extracted features are fed as input to artificial neural network to do prognosis on the data obtained for finding the condition of the motor. Hall Effect based system is used to measure the current signal and the features are extracted and trained to predict the condition of system using MATLAB in real time. The experimental findings reveal that the suggested technique achieves better accuracy in induction motor fault prognosis.
... The working strategy of this model is collection and processing the motor signatures and comparing these signatures in healthy models with faulty models. Of course, these signatures can be current, vibration, noise, heat, etc [5]. Usually, vibration signatures are used in mechanical bearing fault diagnosing [6]. ...
Due to its obvious advantages, there is no doubt that Electric vehicles are gaining huge popularity. Thats due to the advantages of electric vehicles when compared to the traditional ICE vehicles. On the other hand, and as electric vehicles are still machines built by human beings, the EVs have several components that can be faulty and affect the vehicle performance and maybe in some stages lead to severe failures or life threatening. These components are mainly the battery, electric motor and controllers. So, many studies focused on studying the electric vehicles components fault detection in the early stages. In this paper, we will focus on one of the most common motor types, the permanent magnet synchronous motors. One of the common PMSM faults related to the rotor is the demagnetization. This fault will be extensively studied using two of the signal analysis method techniques: the output torque and the stator current signal analysis. Using the MATLAB SimuLink, a complete model of the PMSM will be built and the uniform demagnetization fault will be introduced. Then, a comparison between both torque and current signatures will be presented to put our hands on the ability of these techniques to identify the uniform demagnetization fault.
... There are also several non-intrusive condition monitoring techniques which allow detection of fault using signals that can be sensed externally [9]- [11]. Majority of the non-intrusive methods utilize the motor current signal analysis, which monitors machine health by analysing operating current in time and frequency domain [12], [13]. This technique is suitable for detecting a wide range of stator as well as certain rotor faults. ...
This paper presents a modelling technique to estimate the changes in vibration spectrum due to non-uniform demagnetization. A modulation function is defined that accounts for the nature of demagnetization in the magnets. Using this function and the conformal mapping based approach, the field distribution in the air-gap is obtained. As this approach is semi-analytical, the electromagnetic step of performing vibration spectrum analysis of a machine become faster. The modulation function introduces the effects of magnet demagnetization into the air-gap flux density. The mechanical stress distribution obtained from the field distributions is fed to mechanical harmonic response solver to estimate the level of acceleration corresponding to the particular frequencies of vibration. The results and discussion related to the extent of demagnetization and the corresponding changes in vibration spectrum are presented.
... This method uses the supply current to produce the current signature from frequency spectrum transformation. Faults in motor components produce anomalies in a magnetic field and change the mutual and self-inductance of the motor that appear in the motor supply current spectrum [55,56]. This method allows detecting faults such as [53,57]: ...
Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.
... Motor current signal analysis (MCSA) refers to the process used to obtain information about the dynamic of the electric machine by signal processing from the stator currents. The current signals in the time domain are usually obtained through current sensors with resistive shunts at their outputs [26]. Frequently, researchers obtain the current samples from different speeds, frequencies, and load conditions to acquire behavioral information on the dynamics under different conditions to apply the detection and classification techniques in practical scenarios, such as industrial applications. ...
Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.
