Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Conventional bearing fault diagnosis methods require specialized instruments to acquire signals that can reflect the health condition of the bearing. For instance, an accelerometer is used to acquire vibration signals, whereas an encoder is used to measure motor shaft speed. This study proposes a new method for simplifying the instruments for motor bearing fault diagnosis. Specifically, a video clip recording of a running bearing system is captured using a cellphone that is equipped with a camera and a microphone. The recorded video is subsequently analyzed to obtain the instantaneous frequency of rotation (IFR). The instantaneous fault characteristic frequency (IFCF) of the defective bearing is obtained by analyzing the sound signal that is recorded by the microphone. The fault characteristic order is calculated by dividing IFCF by IFR to identify the fault type of the bearing. The effectiveness and robustness of the proposed method are verified by a series of experiments. This study provides a simple, flexible, and effective solution for motor bearing fault diagnosis. Given that the signals are gathered using an affordable and accessible cellphone, the proposed method is proven suitable for diagnosing the health conditions of bearing systems that are located in remote areas where specialized instruments are unavailable or limited

Indirect multisignal monitoring and diagnosis of drill wear

A machine tool utilisation rate can be improved by an advanced condition monitoring system using modern sensor and signal processing techniques. A drilling test and analysis program for indirect tool wear measurement forms the basis of this thesis. For monitoring the drill wear a number of monitoring methods such as vibration, acoustic emission, sound, spindle power and axial force were tested. The signals were analysed in the time domain using statistical methods such as root mean square (rms) value and maximum. The signals were further analysed using Fast Fourier Transform (FFT) to determine their frequency contents. The effectiveness of the best sensors and analysis methods for predicting the remaining lifetime of a tool in use has been defined. The results show that vibration, sound and acoustic emission measurements are more reliable for tool wear monitoring than the most commonly used measurements of power consumption, current and force. The relationships between analysed signals and tool wear form a basis for the diagnosis system. Higher order polynomial regression functions with a limited number of terms have been developed and used to mimic drill wear development and monitoring parameters that follow this trend. Regression analysis solves the problem of how to save measuring data for a number of tools so as to follow the trend of the measuring signal; it also makes it possible to give a prognosis of the remaining lifetime of the drill. A simplified dynamic model has been developed to gain a better understanding of why certain monitoring methods work better than others. The simulation model also serves the testing of the developed automatic diagnostic method, which is based on the use of simplified fuzzy logic. The simplified fuzzy approach makes it possible to combine a number of measuring parameters and thus improves the reliability of diagnosis. In order to facilitate the handling of varying drilling conditions and work piece materials, the use of neural networks has been introduced in the developed approach. The scientific contribution of the thesis can be summarised as the development of an automatically adaptive diagnostic tool for drill wear detection. The new approach is based on the use of simplified fuzzy logic and higher order polynomial regression analysis, and it relies on monitoring methods that have been tested in this thesis. The diagnosis program does not require a lot of memory or processing power and consequently is capable of handling a great number of tools in a machining centre.reviewe

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Remote machine condition monitoring based on power supply measurements

The most widely used rotating machines in the industry are three phase alternative current (AC) induction machines. With the advances in variable speed drive (VSD) technology, they have become even more reliable than their direct current (DC) counterpart. However, inevitably these motors soon begin to fail with time due to mechanical, electrical or thermal stress hence the need for condition monitoring (CM). Condition monitoring systems help keep machines running productively by detecting potential equipment failures before it actually fails. Many condition monitoring methods exist on the market including vibration monitoring; acoustic emission monitoring, thermal monitoring, chemical monitoring, current monitoring but most of these methods require additional sensors and expensive data acquisition system on top of a specialise software tool. This all increases the cost of ownership and maintenance. For more efficient monitoring of induction motor drive systems, this research investigates an innovative remote monitoring system using existing data available in AC drives based on AC motor operating process. This research uses standard automation components already present in most automated control systems. A remote data communication platform is developed, allowing access to the control data remotely over a wireless network and internet using PLC and SCADA system. Remote machine condition monitoring is not a new idea but its application to machine monitoring based on power supply parameters indirectly measured by an inverter is new. To evaluate the basic performance of the platform, the monitoring of shaft misalignment, a typical fault in mechanical system is investigated using an in-house gearbox test rig. It has resulted in a model based detection method based on different speed and load settings against the motor current feedback read by the inverter. The results have demonstrated that the platform is reliable and effective. In addition the monitoring method can be employed to detect and diagnose different degrees of misalignment in real time. This dissertation has major contributions to knowledge which includes: Understanding of real life machine condition monitoring problems for this application, including use of wireless sensor, communication over Industrial Ethernet and network security. The use of standard automation components (PLC and SCADA) in machine condition monitoring. MSc Research (Engineering) Thesis x An improved gearbox test rig platform which has the capability of remote control, acquiring and transferring data for monitoring induction machine drive system. The presented work shows that any machine using automated components such as PLC and SCADA and incorporating motor drive systems and other actuators has the potential to use the automated components for control, condition monitoring and reporting but this will require more tests to be done using the proposed platform

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Fault detection system for internal combustion engines

Dissertação de mestrado, Engenharia Eléctrica e Electrónica, Instituto Superior de Engenharia, Universidade do Algarve, 2017O desenvolvimento de dispositivos para Internet das Coisas abre novas oportunidades na área de manutenção preditiva. A ideia geral da Internet das Coisas é ligar tudo à Internet, permitindo criar assim sistemas escaláveis suportados pelo processamento remoto, centralizado ou decentralizado. Com a utilização de módulos de baixa potência e de baixo custo, esses sistemas são também eficazes em termos de custo. Alguns estudos indicam que nos próximos anos a manutenção preditiva das máquinas será a principal aplicação da análise de dados na indústria, baseada em Internet das Coisas. Apesar de existirem vários tipos de máquinas, as mais importantes são os motores elétricos e os de combustão interna. Os motores elétricos são amplamente utilizados em diversos setores. Já os motores de combustão interna, por sua vez, são principalmente utilizados nas indústrias automóvel e marítima. Tipicamente, os motores modernos de combustão interna têm uma unidade de controlo computadorizado com capacidade de autodiagnóstico. Contudo, ao contrario da indústria automóvel que utiliza o sistema de On-board diagnostics, a indústria marítima não possui uma norma definida, ou pelo menos uma norma dominante. Isso acarreta a utilização de equipamentos de preço elevado para extrair informação sobre o estado do motor da unidade de controlo. Além disso, a monitorização é intermitente, obrigando ao proprietário de uma embarcação a efetuar a extração dos dados periodicamente para posterior análise. A monitorização das características de operação de um motor um processo fundamental numa manutenção preditiva, sendo que, pela monitorização de um ou mais parâmetros de uma máquina (incluindo a vibração, temperatura, etc.), tenta identificar uma mudança significativa na mesma, que, por sua vez, possa indicar o aparecimento de uma falha. A estratégia de monitorização preditiva assegura que as atividades de manutenção são executadas apenas quando são realmente necessárias, mas para tal é necessário monitorizar periodicamente ou constantemente o equipamento, processar os dados e analisar os resultados. Este método tem, no entanto, as suas desvantagens. O custo do equipamento portátil de monitorização, ou de um sistema estacionário que esteja permanentemente instalado, depende do tipo de variáveis monitorizadas, da precisão de medida, do ambiente de trabalho, ou até do nível de desenvolvimento do sistema. Para além disso, o defeito pode não ser detetado, ou então ser detetado um defeito não existente (um falso positivo, ou uma falsa detecção). Para efectuar uma monitorização preditiva, podem ser utilizados um ou mais métodos de monitorização. Os métodos principais são: análise de vibração, análise de óleo, análise de desempenho, termografia, ferrografia ou análise dos sinais acústicos. De entre todos, a análise de vibração é particularmente interessante, por ser um método não intrusivo e por permitir não só detetar as falhas, mas também classificá-las. Para monitorizar as vibrações de uma máquina utilizam-se transdutores de deslocamento, de velocidade ou de aceleração (acelerómetros). Os transdutores de deslocamento utilizados na indústria permitem medir apenas o deslocamento relativo, o que nem sempre é conveniente. Os preços dos transdutores industriais de velocidade são relativamente altos. Os acelerómetros piezoelétricos industrias também têm um preço elevado. Se as condições ambientais não forem exageradas (por exemplo, temperaturas elevadas) podem ser utilizados acelerómetros MEMS (Microelectromechanical system). Os acelerómetros do tipo MEMS não têm a resposta em frequência tão ampla como os piezoelétricos, no entanto a diferença de preço em relação às restantes opções é muito significativa, e os acelerómetros MEMS, hoje em dia, são praticamente todos digitais, o que simplifica o sistema. Outra alternativa é a película piezoelétrica feita a partir de polyvinylidene difluoride (PVDF). A resposta em frequência desta pelicula é por vezes melhor do que a dos acelerómetros piezoelétricos, com frequências de ressonância acima de 10 MHz. O preço de uma película PVDF depende das dimensões da película, mas é normalmente maior do que o preço de um acelerómetro MEMS, sendo ainda assim muito menor do que um acelerómetro piezoelétrico. É importante referir que há modelos de acelerômetros piezoelétricos que incluem amplificador, mas no caso da película PVDF requerem um circuito de condicionamento do sinal. Para além disso, no caso da película PVDF, tem que ser considerada a sensibilidade à interferência eletromagnética. Dado este contexto, o trabalho realizado nesta Tese resulta de uma proposta feita por uma empresa interessada em automatizar este processo, que entrou em contato com universidade para encontrar uma possível solução. O sistema desenvolvido baseia-se no conceito Internet das Coisas, efetuando a monitorização autónoma da condição do motor, de forma independente do sistema de autodiagnóstico que possa existir. Este trabalho tem, genericamente, três objetivos: (i) desenvolver um módulo de contabilização do número de horas de funcionamento do motor; (ii) desenvolver um módulo de monitorização de ocorrência de falhas num motor de combustão interna através da medição e análise de vibrações; (iii) investigar e analisar os resultados de monitorização, para identificar desvios de parâmetros de funcionamento e detetar de forma preditiva a ocorrência de falhas (antes que as mesmas ocorram realmente). O sistema, que compreende os módulos (i) e (ii), será montado no motor, comunicando com um dispositivo do cliente (por exemplo, um smartphone ou tablet) através de uma interface sem fios. Neste caso optou-se por utilizar uma ligação sem fios baseada na tecnologia Bluetooth Low Energy. Para implementação do módulo de contabilização do número de horas de funcionamento, recorreu-se a um sensor do tipo MEMS, por ter um consumo muito baixo. Já o módulo de módulo de monitorização de ocorrência de falhas recorre a um sensor do tipo PVDF, por permitir elevadas definições temporais na captura do sinal de vibração. Para efectuar a análise de dados que permite a identificação da ocorrência de falhas, foram testados e comparados um conjunto de 16 algoritmos de análise conjunta de dados no tempo e na frequência, suportando a identificação de falhas e a diferenciação entre tipos de falhas. Os algoritmos foram avaliados com base em dados reais, obtidos por falhas induzidas num motor de combustão interna, permitindo por essa via encontrar os algoritmos que melhor servem o objetivo proposto

Failure Analysis Of Rotating Equipment Using Vibration Studies And Signal Processing Techniques

This thesis focuses on failure analysis of rotating machines based on vibration analysis and signal processing techniques. The main objectives are: identifying machine’s condition, determining the faults specific response, creating methods to correct the faults, and investigating available statistical analysis methods for automatic fault detection and classification. In vibration analysis, the accelerometer data is analyzed in time and frequency domain which will determine the machine’s condition by identifying the characteristic frequencies of the faults. These fault frequencies are specific for each type of machine’s faults. Therefore, they are referred to as faults’ signatures. The most common faults of the rotating machines are unbalanced load torque, misaligned shaft, looseness, and bearing faults. The second objective is to find correction methods for rectifying the faulty situations. Therefore, correction methods for the unbalanced condition are comprehensively studied and a novel method for balancing an unbalanced rotor is developed which is based on image processing methods and results in lowering machine’s vibrations. Another objective of this research is to collect huge amount of vibration data and implement statistical data analysis methods to categorize different machine’s conditions. Therefore, principal components analysis, K-nearest neighbor, and singular value decomposition are implemented to identify different faults of the rotating machines automatically. The statistical methods have demonstrated high precision in classifying different faulty situations. Fault identification at early stages will enhance machine’s health and reduces the maintenance costs significantly. The statistical methods are easy to implement, and have disaffected the need for an expert maintenance engineer and will identify the machine’s fault automatically

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

Improved micro-contact resistance model that considers material deformation, electron transport and thin film characteristics

This paper reports on an improved analytic model forpredicting micro-contact resistance needed for designing microelectro-mechanical systems (MEMS) switches. The originalmodel had two primary considerations: 1) contact materialdeformation (i.e. elastic, plastic, or elastic-plastic) and 2) effectivecontact area radius. The model also assumed that individual aspotswere close together and that their interactions weredependent on each other which led to using the single effective aspotcontact area model. This single effective area model wasused to determine specific electron transport regions (i.e. ballistic,quasi-ballistic, or diffusive) by comparing the effective radius andthe mean free path of an electron. Using this model required thatmicro-switch contact materials be deposited, during devicefabrication, with processes ensuring low surface roughness values(i.e. sputtered films). Sputtered thin film electric contacts,however, do not behave like bulk materials and the effects of thinfilm contacts and spreading resistance must be considered. Theimproved micro-contact resistance model accounts for the twoprimary considerations above, as well as, using thin film,sputtered, electric contact