Characterization of brushed DC motor with brush fault using thermal assessment

Direct current motors (DC motor) are used in the small electric devices commonly. Brushed DC motors are cheap and easy to install, thus their popularity. Although the popularity, faults occur which make diagnosis and detection of faults very important. It avoids financial loss and unexpected shutdown operation causes by these faults. This paper is a present characterization of brushed DC motor with brush fault using thermal signature analysis. To organize the character, the temperature profile of DC motor was analysed using the K-type thermocouple with data logger. The thermocouples were mounted on 4 part of the DC motor, casing, permanent magnet, brush and bearing. The temperature data of DC motor with faulty brush and healthy DC motor were measured by thermocouple and recorded using data logger in real time until steady state temperature, under different load. The analysis on the steady state temperature of brush fault can be conclude through recognisable of characteristics temperature difference with a healthy motor

Implementation of Pipelined FFT Processor on FPGA Microchip Proposed for Mechanical Applications / Siti Lailatul Mohd Hassan...[et al.]

Fast Fourier transform (FFT) is an efficient algorithm for discrete Fourier transform (DFT) which computes any signal in time domain into frequency domain. FFT processor is a hardware implementation for FFT algorithm. This processor is widely used in many applications such as wireless sensor networks, medical imaging, geophysics and mechanical applications. These applications require a low power, high speed and small area processor. Pipelined FFT is well known for the highly fast calculation and high precision computation, making it a more reliable FFT to be used in lots of applications. It also requires less hardware, as it uses less multiplier than conventional FFT, minimizing both logic hardware and memory volume. This paper provides a survey on hardware utilization and performance for different size pipelined FFT implemented on a FPGA microchip for 64-point, 128-point and 256-point. This FFT can be used in various applications such as mechanical machinery maintenance system (MMMS). The result shows low total thermal power dissipation and high processing capabilities for all size pipelined FFT studied in this paper. However, bigger size pipelined FFT, requires more design area and memory. In this paper, the biggest size pipelined FFT, only used 7% of the overall total logic elements. It can be concluded that any size pipelined FFT has low power consumption capabilities with high speed performance suitable with any application mentioned earlier

A novel ensemble clustering for operational transients classification with application to a nuclear power plant turbine

International audienceThe objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then, a Spectral Clustering technique of literature, embedding the unsupervised K-Means algorithm, is applied to the co-association matrix for finding the optimum number of clusters of the final consensus clustering, based on Silhouette validity index calculation. The proposed approach is developed with reference to an artificial case study, properly designed to mimic the signal trend behavior of a Nuclear Power Plant (NPP) turbine during shutdown. The results of the artificial case have been compared with those achieved by a state-of-art approach, known as Cluster-based Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPA-METIS). The comparison shows that the proposed approach is able to identify a final consensus clustering that classifies the transients with better accuracy and robustness compared to the CSPA-METIS approach. The approach is, then, validated on an industrial case concerning 149 shutdown transients of a NPP turbine

Deteksi Ketidaknormalan Kopling Pada Motor Induksi Menggunakan Sensor Accelerometer Berbasis Raspberry PI

Motor induksi sangat banyak digunakan dalam kehidupan sehari hari kita. Baik itu digunakan di industri maupun di rumah tangga karena kontruksinya sederhana, pengoperasian mudah dan mempunyai kecepatan yang relative konstan. Pemeliharaan/perawatan mesin sangat dibutuhkan untuk memperpanjang umur pemakaian motor induksi. Tindakan preventif harus didahulukan untuk mencegah kerusakan yang lebih besar pada mesin. Salah satu tindakan preventif yang dapat dilakukan adalah monitoring vibrasi pada mesin. Kerusakan pada motor induksi yang tidak terdeteksi pada tahap awal dapat menyebabkan kerusakan lebih parah. Kerusakan motor di industri dapat mengakibatkan kerugian yang sangat besar karena proses produksi berhenti. Dalam penelitian ini, digunakan sensor accelerometer untuk melihat pola vibrasi sinyal dengan menggunakan Fast Fourier Transform (FFT) dan Neural Network terhadap jenis-jenis kondisi abnormal. Ada tiga jenis kondisi abnormal yang dibahas dalam penelitian ini yaitu misalignment, unbalance dan looseness. Memonitoring keadaan mesin listrik secara kontinyu sehingga keadaan abnormal pada motor listrik dapat diketahui secara dini. Sistem dapat mendeteksi jenis jenis kerusakan secara on-line pada ketidaknormalan kopling motor listrik yaitu unbalance, misalignment dan looseness. Tingkat keberhasilan neural network dalam sistem ini mencapai 80 persen

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

Fault Detection and Prediction in Elevators Using FFT-based Features

The purpose of this study is to find out how Fast Fourier Transform (FFT) based features could be used in fault detection and prediction in elevators. The overall main objective is to improve on the existing maintenance systems. The data we analyzed in this work were obtained through applying FFT on vertical vibration signal of each elevator movement. The goal was to study the trend of data over time and detect any significant change that can indicate potential faulty behaviors inside elevators. Due to the challenges in analyzing a stream of high-dimensional FFT data, we decided to utilize two dimensionality reduction techniques, namely Feature selection with Dominant frequencies analysis and Feature extraction with Autoencoder. After that, we used change point detection on the newly acquired features to detect significant changes. For validation, we first observed the FFT spectrum of each elevator and singled out the ones that contain clear visual changes. For these elevators, we estimated the visual change points and had them as the target outputs for our algorithms. The goal was to see if our implementation of feature selection and feature extraction, combined with change point detection could correctly the target change points. Final results showed that all significant visual changes in the original spectrums could be detected through the use of feature selection and feature extraction, together with change point detection. Furthermore, we were able to calculate the percentage change in mean vibration amplitude of elevators to determine the most problematic cases with high increases in vibration level. These findings indicated that FFT based features can be used in identifying potential faulty behaviors in elevator systems and the techniques used in this work have shown promising results

Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

Engine ignition patterns can be analyzed to identify the engine fault according to both the specific prior domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous-fault diagnosis. Another challenge is the acquisition of a large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier, namely, pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses and is superior to the existing approach

Application of variational mode decomposition in vibration analysis of machine components

Monitoring and diagnosis of machinery in maintenance are often undertaken using vibration analysis. The machine vibration signal is invariably complex and diverse, and thus useful information and features are difficult to extract. Variational mode decomposition (VMD) is a recent signal processing method that able to extract some of important features from machine vibration signal. The performance of the VMD method depends on the selection of its input parameters, especially the mode number and balancing parameter (also known as quadratic penalty term). However, the current VMD method is still using a manual effort to extract the input parameters where it subjects to interpretation of experienced experts. Hence, machine diagnosis becomes time consuming and prone to error. The aim of this research was to propose an automated parameter selection method for selecting the VMD input parameters. The proposed method consisted of two-stage selections where the first stage selection was used to select the initial mode number and the second stage selection was used to select the optimized mode number and balancing parameter. A new machine diagnosis approach was developed, named as VMD Differential Evolution Algorithm (VMDEA)-Extreme Learning Machine (ELM). Vibration signal datasets were then reconstructed using VMDEA and the multi-domain features consisted of time-domain, frequency-domain and multi-scale fuzzy entropy were extracted. It was demonstrated that the VMDEA method was able to reduce the computational time about 14% to 53% as compared to VMD-Genetic Algorithm (GA), VMD-Particle Swarm Optimization (PSO) and VMD-Differential Evolution (DE) approaches for bearing, shaft and gear. It also exhibited a better convergence with about two to nine less iterations as compared to VMD-GA, VMD-PSO and VMD-DE for bearing, shaft and gear. The VMDEA-ELM was able to illustrate higher classification accuracy about 11% to 20% than Empirical Mode Decomposition (EMD)-ELM, Ensemble EMD (EEMD)-ELM and Complimentary EEMD (CEEMD)-ELM for bearing shaft and gear. The bearing datasets from Case Western Reserve University were tested with VMDEA-ELM model and compared with Support Vector Machine (SVM)-Dempster-Shafer (DS), EEMD Optimal Mode Multi-scale Fuzzy Entropy Fault Diagnosis (EOMSMFD), Wavelet Packet Transform (WPT)-Local Characteristic-scale Decomposition (LCD)- ELM, and Arctangent S-shaped PSO least square support vector machine (ATSWPLM) models in term of its classification accuracy. The VMDEA-ELM model demonstrates better diagnosis accuracy with small differences between 2% to 4% as compared to EOMSMFD and WPT-LCD-ELM but less diagnosis accuracy in the range of 4% to 5% as compared to SVM-DS and ATSWPLM. The diagnosis approach VMDEA-ELM was also able to provide faster classification performance about 6 40 times faster than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). This study provides an improved solution in determining an optimized VMD parameters by using VMDEA. It also demonstrates a more accurate and effective diagnostic approach for machine maintenance using VMDEA-ELM