Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain

© 2016 Juan Jose Saucedo-Dorantes et al. Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.Postprint (published version

PENGEMBANGAN SISTEM PEMANTAUAN KONDISI UNTUK KESELAMATAN ROTATING MACHINE DI PWR DENGAN MOTOR CURRENT SIGNATURE ANALYSIS

Pemantauan kondisi rotating machine sangat diperlukan untuk menjamin keselamatan operasi sekaligus untuk meningkatkan efisiensi operasi di PWR. Salah satu teknik pemantauan kondisi terbaik yang dewasa ini dipilih karena mudah, non-invasive dan murah dalam implementasinya adalah Motor Current Signature Analysis (MCSA). Namun sayangnya penelitian aplikasi teknik ini untuk perangkat keras yang compact, low cost, berkelas industri dan layak untuk aplikasi pembangkit daya bertenaga nuklir sangat terbatas. Penelitian ini bertujuan untuk mengembangkan metode pemantauan kondisi berbasis MCSA dengan perangkat keras berkelas industri yang kompak untuk pembangkit daya tenaga nuklir. Penelitian meliputi aspek pengembangan perangkat keras real-time berbasis FPGA-CompactRIO, pembuatan modul untuk penampil early warning, pengujian unjuk kerja algoritma perangkat kerasnya, analisis spektrum berbagai kerusakan komponen motor elektrik, serta pengujian kinerjanya dalam mendeteksi berbagai kerusakan. Sistem pemantauan mampu mengeksekusi dengan total waktu eksekusi berkisar 164 ms, berhasil mendeteksi spektrum frekuensi berbagai kerusakan di motor induksi seperti stator shorted turn berkisar 75%, rotor broken bar 95%, eccentricity 65%, dan mechanical misalignment 85%, termasuk gangguan catu daya voltage unbalance 100%. Berdasarkan unjuk kerja perangkatnya, sistem pemantauan kondisi rotating machine ini menjadi salah satu alternatif terbaik untuk sistem pemantauan berbagai perangkat pemantauan di reaktor nuklir.Kata kunci : Pemantauan kondisi, rotating machine, Motor Current Signature Analysis (MCSA), Field Programmable Gate Array (FPGA) Condition monitoring of rotating machine is essential to guarantee the safety operation as well as to improve the efficiency of nuclear power plants operations. One of the promising condition monitoring techniques which has been preferred currently since it is simple, non-invasive and inexpensive is Motor Stator Signature Analysis (MCSA). However, the investigation of the MCSA technique using a compact, low cost, and having industrial class hardware which is capable for nucear power plant applications has been limited. The research is aimed to develop condition monitoring method based on MCSA utilizing a compact industrial class for nuclear power plant. The investigation includes development of condition monitoring based on real-time FPGA-CompatRIO hardware, development of a custom built display module for early warning system, testing of the monitoring hardware, fault frequency analysis of electric motors including the performances of fault detections. The condition monitoring system is able to execute a fault detection task around 164 ms, to recognize accurately fault frequencies of stator shorted turn for about 75%, broken rotor bar around 95%, eccentricity 65%, mechanical misalignment 85%, including supply voltage unbalances 100%. The condition monitoring system based on its performance assessments could become a suitable alternative not only for rotating machines but also condition monitoring for other nuclear reactor components. Keywords : Condition monitoring, rotating machine, Motor Current Signature Analysis (MCSA), Field Programmable Gate Array (FPGA

A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node

Procesamiento digital de señales en FPGA para análisis de alta velocidad en entornos industriales

Este trabajo propone un sistema de procesamiento de información en alta velocidad cuyo diseño está distribuido entre partes de software, hardware y firmware, buscando mostrar la utilidad de incluir sistemas basados en FPGA (Field Programmable Gate Array) en aplicaciones industriales donde se requiera un procesamiento rápido, por ejemplo para el procesamiento digital de señales (Digital Signal Processing, DSP). Así se muestra que el procesamiento realizado en la FPGA puede ser complementado con un tratamiento en una PC, en la que se puede aprovechar la potencia y funcionalidades especiales de herramientas de software estándar tales como Matlab®. El vínculo entre las plataformas en este caso se basa en una comunicación Ethernet que se implementa en la FPGA mediante una descripción VHDL de uso libre que se ha desarrollado para este trabajo. Para la conexión física se ha construido una interfaz de adaptación entre puertos de E/S de la FPGA y un puerto Ethernet. Finalmente, en la PC se administra la comunicación mediante una conexión Ethernet/IP/UDP convencional, lo que da versatilidad al diseño dado que permite independizarse del lenguaje de programación y ser aprovechado en otras aplicaciones específicas. Se presenta un caso de estudio para el cual se utiliza un algoritmo basado en el cálculo de la transformada de Fourier (Fast Fourier Transformation, FFT) para la determinación de la velocidad de giro de un motor eléctrico a partir de señales vibratorias captadas.Sociedad Argentina de Informática e Investigación Operativ

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals

Tesis por compendio[ES] En la actualidad, el desarrollo y aplicación de algoritmos para el reconocimiento de patrones que mejoren los niveles de rendimiento, detección y procesamiento de datos en diferentes áreas del conocimiento resulta un tema de gran interés. En este contexto, y específicamente en relación con la aplicación de estos algoritmos en el monitoreo y diagnóstico de máquinas eléctricas, el uso de señales de flujo es una alternativa muy interesante para detectar las diferentes fallas. Asimismo, y en relación con el uso de señales biomédicas, es de gran interés extraer características relevantes en las señales de actigrafía para la identificación de patrones que pueden estar asociados con una patología específica. En esta tesis, se han desarrollado y aplicado algoritmos basados en el procesamiento estadístico y espectral de señales, para la detección y diagnóstico de fallas en máquinas eléctricas, así como su aplicación al tratamiento de señales de actigrafía. Con el desarrollo de los algoritmos propuestos, se pretende tener un sistema dinámico de indicación e identificación para detectar la falla o la patología asociada que no depende de parámetros o información externa que pueda condicionar los resultados, sólo de la información primaria que inicialmente presenta la señal a tratar (como la periodicidad, amplitud, frecuencia y fase de la muestra). A partir del uso de los algoritmos desarrollados para la detección y diagnóstico de fallas en máquinas eléctricas, basados en el procesamiento estadístico y espectral de señales, se pretende avanzar, en relación con los modelos actualmente existentes, en la identificación de fallas mediante el uso de señales de flujo. Además, y por otro lado, mediante el uso de estadísticas de orden superior, para la extracción de anomalías en las señales de actigrafía, se han encontrado parámetros alternativos para la identificación de procesos que pueden estar relacionados con patologías específicas.[CA] En l'actualitat, el desenvolupament i aplicació d'algoritmes per al reconeixement de patrons que milloren els nivells de rendiment, detecció i processament de dades en diferents àrees del coneixement és un tema de gran interés. En aquest context, i específicament en relació amb l'aplicació d'aquests algoritmes a la monitorització i diagnòstic de màquines elèctriques, l'ús de senyals de flux és una alternativa molt interessant per tal de detectar les diferents avaries. Així mateix, i en relació amb l'ús de senyals biomèdics, és de gran interés extraure característiques rellevants en els senyals d'actigrafia per a la identificació de patrons que poden estar associats amb una patologia específica. En aquesta tesi, s'han desenvolupat i aplicat algoritmes basats en el processament estadístic i espectral de senyals per a la detecció i diagnòstic d'avaries en màquines elèctriques, així com la seua aplicació al tractament de senyals d'actigrafia. Amb el desenvolupament dels algoritmes proposats, es pretén obtindre un sistema dinàmic d'indicació i identificació per a detectar l'avaria o la patologia associada, el qual no depenga de paràmetres o informació externa que puga condicionar els resultats, només de la informació primària que inicialment presenta el senyal a tractar (com la periodicitat, amplitud, freqüència i fase de la mostra). A partir de l'ús dels algoritmes desenvolupats per a la detecció i diagnòstic d'avaries en màquines elèctriques, basats en el processament estadístic i espectral de senyals, es pretén avançar, en relació amb els models actualment existents, en la identificació de avaries mitjançant l'ús de senyals de flux. A més, i d'altra banda, mitjançant l'ús d'estadístics d'ordre superior, per a l'extracció d'anomalies en els senyals d'actigrafía, s'han trobat paràmetres alternatius per a la identificació de processos que poden estar relacionats amb patologies específiques.[EN] Nowadays, the development and application of algorithms for pattern recognition that improve the levels of performance, detection and data processing in different areas of knowledge is a topic of great interest. In this context, and specifically in relation to the application of these algorithms to the monitoring and diagnosis of electrical machines, the use of stray flux signals is a very interesting alternative to detect the different faults. Likewise, and in relation to the use of biomedical signals, it is of great interest to extract relevant features in actigraphy signals for the identification of patterns that may be associated with a specific pathology. In this thesis, algorithms based on statistical and spectral signal processing have been developed and applied to the detection and diagnosis of failures in electrical machines, as well as to the treatment of actigraphy signals. With the development of the proposed algorithms, it is intended to have a dynamic indication and identification system for detecting the failure or associated pathology that does not depend on parameters or external information that may condition the results, but only rely on the primary information that initially presents the signal to be treated (such as the periodicity, amplitude, frequency and phase of the sample). From the use of the algorithms developed for the detection and diagnosis of failures in electrical machines, based on the statistical and spectral signal processing, it is intended to advance, in relation to the models currently existing, in the identification of failures through the use of stray flux signals. In addition, and on the other hand, through the use of higher order statistics for the extraction of anomalies in actigraphy signals, alternative parameters have been found for the identification of processes that may be related to specific pathologies.Iglesias Martínez, ME. (2020). Development of algorithms of statistical signal processing for the detection and pattern recognitionin time series. Application to the diagnosis of electrical machines and to the features extraction in Actigraphy signals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/145603TESISCompendi