Diagnosis of Rotor Asymmetries Faults in Induction Machines Using the Rectified Stator Current

(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Fault diagnosis of induction motors through the analysis of the stator current is increasingly being used in maintenance systems, because it is non-invasive and has low requirements of hardware and software. Nevertheless, its industrial application faces some practical limitations. In particular, the detection of fault harmonics that are very close to the fundamental component is challenging, as in large induction motors working at very low slip, because the leakage of the fundamental can hide the fault components until the damage is severe. Several methods have been proposed to alleviate this problem, although all of them increase noticeably the complexity of the diagnostic system. In this paper, a novel method is proposed, based on the analysis of the rectified motor current. It is shown that its spectrum contains the same fault harmonics as the spectrum of the original current signal, but with a much lower frequency, and free from the fundamental component leakage. Besides, the proposed method is very easy to implement, either by software, using the absolute value of the current samples, or by hardware, using a simple rectifier. The proposed approach is presented theoretically and validated experimentally with the detection of a broken bars fault of a large induction motor.This work was supported in part by the Spanish "Ministerio de Ciencia, Innovacion yUniversidades (MCIU)," in part by the "Agencia Estatal de Investigacion (AEI)," and in part by the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018," under Project RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J. (2020). Diagnosis of Rotor Asymmetries Faults in Induction Machines Using the Rectified Stator Current. IEEE Transactions on Energy Conversion. 35(1):213-221. https://doi.org/10.1109/TEC.2019.2951008S21322135

Induction motors fault diagnosis using machine learning and advanced signal processing techniques

In this thesis, induction motors fault diagnosis are investigated using machine learning and advanced signal processing techniques considering two scenarios: 1) induction motors are directly connected online; and 2) induction motors are fed by variable frequency drives (VFDs). The research is based on experimental data obtained in the lab. Various single- and multi- electrical and/or mechanical faults were applied to two identical induction motors in experiments. Stator currents and vibration signals of the two motors were measured simultaneously during experiments and were used in developing the fault diagnosis method. Signal processing techniques such as Matching Pursuit (MP) and Discrete Wavelet Transform (DWT) are chosen for feature extraction. Classification algorithms, including decision trees, support vector machine (SVM), K-nearest neighbors (KNN), and Ensemble algorithms are used in the study to evaluate the performance and suitability of different classifiers for induction motor fault diagnosis. Novel curve or surface fitting techniques are implemented to obtain features for conditions that have not been tested in experiments. The proposed fault diagnosis method can accurately detect single- or multi- electrical and mechanical faults in induction motors either directly online or fed by VFDs. In addition to the machine learning method, a threshold method using the stator current signal processed by DWT is also proposed in the thesis

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Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications

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Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions

An intelligent monitoring system for online induction motor fault diagnostics

For more than a century, the induction motor (IM) has been the powerhouse industrial applications such as machine tools, manufacturing facilities, pumping stations, and more recently, in electric vehicles. In addition, IMs account for approximately 40%- 45% of the annual global electricity consumption. Therefore it is a critical issue to improve IM operation efficiency and reliability. In applications, unexpected failures of IMs can result in extensive production loss and increased costs. The classical preventive maintenance procedures involve periodic stoppages of IMs for inspection. If such procedures result in no faults found in the machine, as is common in practice, the unnecessary downtimes will increase operational costs significantly. This inefficiency can be addressed by condition monitoring, whereby sensors relay information about the IM in real-time, allowing for incipient IM fault diagnosis. Such a process involves three general stages: • Data acquisition: A process to collect data using appropriate sensors. • Fault detection: A means to process collected data, extract representative fault features, and determine the condition of the motor components. • Fault classification: A means to automatically classify fault data to allow decision-making on whether or not the motor is healthy or damaged. However, there are challenges with the above stages that are at present, barriers to the industrial adoption of condition monitoring, such as: • Implementation limitations of traditional wired sensors in industrial plants. • The restrictive memory and range capabilities of existing commercial wireless sensors. • Challenges related to misleading representative fault signals and means to quantify the fault features. • A means to adaptively classify the data without prior knowledge given to a fault classification system. To address these challenges, the objective of this work is to develop a smart sensor-based IM fault diagnostic system targeted for real industrial applications. Specific projects pertaining to this objective include the following: Smart sensor-based wireless data acquisition systems: A smart sensor network including current and vibration sensors, which are compact, inexpensive, lowpower, and longer-range wireless transmission. • Fault detection: A new method to more reliably extract the representative fault features, applicable under all IM loading conditions. • Fault quantification: A new means to transform fault features into a monitoring fault index. • Fault classification: An evolving classification system developed to track and identify groups of fault index information for automatic IM health condition monitoring. Results show that: (1) the wireless smart sensors are able to effectively collect data from the induction motor, (2) the fault detection and quantification techniques are able to efficiently extract representative fault features, and (3) the online diagnostic classifier diagnoses the induction motor condition with an average accuracy of 99.41%

NASA Tech Briefs, September 2011

Topics covered include: Fused Reality for Enhanced Flight Test Capabilities; Thermography to Inspect Insulation of Large Cryogenic Tanks; Crush Test Abuse Stand; Test Generator for MATLAB Simulations; Dynamic Monitoring of Cleanroom Fallout Using an Air Particle Counter; Enhancement to Non-Contacting Stress Measurement of Blade Vibration Frequency; Positively Verifying Mating of Previously Unverifiable Flight Connectors; Radiation-Tolerant Intelligent Memory Stack - RTIMS; Ultra-Low-Dropout Linear Regulator; Excitation of a Parallel Plate Waveguide by an Array of Rectangular Waveguides; FPGA for Power Control of MSL Avionics; UAVSAR Active Electronically Scanned Array; Lockout/Tagout (LOTO) Simulator; Silicon Carbide Mounts for Fabry-Perot Interferometers; Measuring the In-Process Figure, Final Prescription, and System Alignment of Large; Optics and Segmented Mirrors Using Lidar Metrology; Fiber-Reinforced Reactive Nano-Epoxy Composites; Polymerization Initiated at the Sidewalls of Carbon Nanotubes; Metal-Matrix/Hollow-Ceramic-Sphere Composites; Piezoelectrically Enhanced Photocathodes; Iridium-Doped Ruthenium Oxide Catalyst for Oxygen Evolution; Improved Mo-Re VPS Alloys for High-Temperature Uses; Data Service Provider Cost Estimation Tool; Hybrid Power Management-Based Vehicle Architecture; Force Limit System; Levitated Duct Fan (LDF) Aircraft Auxiliary Generator; Compact, Two-Sided Structural Cold Plate Configuration; AN Fitting Reconditioning Tool; Active Response Gravity Offload System; Method and Apparatus for Forming Nanodroplets; Rapid Detection of the Varicella Zoster Virus in Saliva; Improved Devices for Collecting Sweat for Chemical Analysis; Phase-Controlled Magnetic Mirror for Wavefront Correction; and Frame-Transfer Gating Raman Spectroscopy for Time-Resolved Multiscalar Combustion Diagnostics

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Scan time reduction of PLCs by dedicated parallel-execution multiple PID controllers using an FPGA

A programmable logic controller (PLC) executes a ladder diagram (LD) using input and output modules. An LD also has PID controller function blocks. It contains as many PID function blocks as the number of process parameters to be controlled. Adding more process parameters slows down PLC scan time. Process parameters are measured as analog signals. The analog input module in the PLC converts these analog signals into digital signals and forwards them to the PID controller as inputs. In this research work, a field-programmable gate array (FPGA)-based multiple PID controller is proposed to retain PLC scan time at a lower value. Concurrent execution of multiple PID controllers was assured by assigning separate FPGA hardware resources for every PID controller. Digital input to the PID controller is routed by the novel idea of analog to digital conversion (ADC), performed using a digital to analog converter (DAC), comparator, and FPGA. ADC combined with dedicated PID controller logic in an FPGA for every closed-loop control system confirms concurrent execution of multiple PID controllers. The time required to execute two closed-loop controls was identified as 18.96000004 ms. This design can be used either with or without a PLC.Web of Science2212art. no. 458