Machine learning-based fault detection and diagnosis in electric motors

Fault diagnosis is critical to any maintenance industry, as early fault detection can prevent catastrophic failures as well as a waste of time and money. In view of these objectives, vibration analysis in the frequency domain is a mature technique. Although well established, traditional methods involve a high cost of time and people to identify failures, causing machine learning methods to grow in recent years. The Machine learning (ML) methods can be divided into two large learning groups: supervised and unsupervised, with the main difference between them being whether the dataset is labeled or not. This study presents a total of four different methods for fault detection and diagnosis. The frequency analysis of the vibration signal was the first approach employed. This analysis was chosen to validate the future results of the ML methods. The Gaussian Mixture model (GMM) was employed for the unsupervised technique. A GMM is a probabilistic model in which all data points are assumed to be generated by a finite number of Gaussian distributions with unknown parameters. For supervised learning, the Convolution neural network (CNN) was used. CNNs are feedforward networks that were inspired by biological pattern recognition processes. All methods were tested through a series of experiments with real electric motors. Results showed that all methods can detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar, and bearing fault condition. Although all approaches are able to identify the fault, each technique has benefits and limitations that make them better for certain types of applications, therefore, a comparison is also made between the methods.O diagnóstico de falhas é fundamental para qualquer indústria de manutenção, a detecção precoce de falhas pode evitar falhas catastróficas, bem como perda de tempo e dinheiro. Tendo em vista esses objetivos, a análise de vibração através do domínio da frequência é uma técnica madura. Embora bem estabelecidos, os métodos tradicionais envolvem um alto custo de tempo e pessoas para identificar falhas, fazendo com que os métodos de aprendizado de máquina cresçam nos últimos anos. Os métodos de Machine learning (ML) podem ser divididos em dois grandes grupos de aprendizagem: supervisionado e não supervisionado, sendo a principal diferença entre eles é o conjunto de dados que está rotulado ou não. Este estudo apresenta um total de quatro métodos diferentes para detecção e diagnóstico de falhas. A análise da frequência do sinal de vibração foi a primeira abordagem empregada. foi escolhida para validar os resultados futuros dos métodos de ML. O Gaussian Mixture Model (GMM) foi empregado para a técnica não supervisionada. O GMM é um modelo probabilístico em que todos os pontos de dados são considerados gerados por um número finito de distribuições gaussianas com parâmetros desconhecidos. Para a aprendizagem supervisionada, foi utilizada a Convolutional Neural Network (CNN). CNNs são redes feedforward que foram inspiradas por processos de reconhecimento de padrões biológicos. Todos os métodos foram testados por meio de uma série de experimentos com motores elétricos reais. Os resultados mostraram que todos os métodos podem detectar e classificar os motores em várias condições de operação induzida: íntegra, desequilibrado, folga mecânica, desalinhamento, eixo empenado, barra quebrada e condição de falha do rolamento. Embora todas as abordagens sejam capazes de identificar a falha, cada técnica tem benefícios e limitações que as tornam melhores para certos tipos de aplicações, por isso, também e feita uma comparação entre os métodos

On the Use of Vibration Analysis for Contact Fault Detection in High-Voltage HVCBs

Abstract As high-voltage circuit breakers (HVCBs) are responsible for switching off the load in the event of anomalies, they suffer various wear and tear, both on their main contacts and on the other actuation mechanisms. Not only load maneuvers but also weather conditions can bring factors that contribute to deterioration and, consequently, contribute to failures of this component that is so important for energy supply. Both failures and maintenance shutdowns generate costs for substations, something that could be minimized if there was monitoring of the condition of the HVCBs. This paper shows a methodology to analyze the vibration signal of HVCB in order to identify and quantify contact failures. The proposed methodology is verified through an experimental setup. The results show that it is possible not only to identify the fault but also to assess its intensity using vibration analysis

Proposal of a System to Identify Failures and Evaluate the Efficiency of Internal Combustion Engines of Thermal Power Plants

Thermoelectric plants are one of the main forms of energy generation in the world, being the second main source of generation in Brazil. However, with rising fuel costs and greater concern for the environment, controlling the efficiency levels of these plants has become critical. This work presents a system to identify failures and evaluate the efficiency of internal combustion engines used in thermal power plants. To assess efficiency, the developed system monitors subsystem losses (such as cooling, lubrication, turbocharger, etc.). In addition, sensors for cylinder pressure and instantaneous speed were installed and comprise an online monitoring system for the pressure condition of each cylinder of the engines. All this is combined into a supervisory system that presents the Sankey diagram of the engine as its main information online and remotely. To validate the system, experiments were carried out in a controlled configuration (where faults were purposely inserted) and in a Brazilian thermal power plant. The results show that by using in-cylinder pressure and the WOIS database, it was possible to detect the presence of a fault as well as pinpoint its location

An Ultrasonic-Capacitive System for Online Characterization of Fuel Oils in Thermal Power Plants

This paper presents a ultrasonic-capacitive system for online analysis of the quality of fuel oils (FO), which are widely used to produce electric energy in Thermal Power Plants (TPP) due to their elevated heating value. The heating value, in turn, is linked to the quality of the fuel (i.e., the density and the amount of contaminants, such as water). Therefore, the analysis of the quality is of great importance for TPPs, either in order to avoid a decrease in generated power or in order to avoid damage to the TPP equipment. The proposed system is composed of two main strategies: a capacitive system (in order to estimate the water content in the fuel) and an ultrasonic system (in order to estimate the density). The conjunction of the two strategies is used in order to estimate the heating value of the fuel, online, as it passes through the pipeline and is an important tool for the TPP in order to detect counterfeit fuel. In addition, the ultrasonic system allows the estimation of the flow rate through the pipeline, hence estimating the amount of oil transferred and obtaining the total mass transferred as a feature of the system. Experimental results are provided for both sensors installed in a TPP in Brazil