Motor Noise and Vibration Test Research

Some factors, such as friction, vibration, and so on, can result in the fault and abnormal noise in the motor. Based on the detection and analysis of noise and vibration, we can identify and eliminate the faults of the motor. This is helpful not only to ensure the completion of production tasks, but also to prevent accidents. In this paper, we briefly introduce the motor noise generation principle. A laptop computer and LabVIEW software are used to design the experiment system to detect and analysis the noise and vibration of motor. External microphone and computer with sound card constitute noise detection system hardware. Vibration sensor and the data acquisition card constitute vibration detection system hardware. LabVIEW software combined with FFT analysis is used to realize the noise signal acquisition, recording and spectral analysis. Detecting and analyzing the noise of the permanent magnet DC motor and three-phase asynchronous motor proves that the motor noise and vibration detecting experimental platform is fully meet the requirements of motor test and research. This detection and analysis system has a good man-machine interface and strong operability

Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents

Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system

Stator and Rotor Faults Diagnosis of Squirrel Cage Motor Based on Fundamental Component Extraction Method

Nowadays, stator current analysis used for detecting the incipient fault in squirrel cage motor has received much attention. However, in the case of interturn short circuit in stator, the traditional symmetrical component method has lost the precondition due to the harmonics and noise; the negative sequence component (NSC) is hard to be obtained accurately. For broken rotor bars, the new added fault feature blanked by fundamental component is also difficult to be discriminated in the current spectrum. To solve the above problems, a fundamental component extraction (FCE) method is proposed in this paper. On one hand, via the antisynchronous speed coordinate (ASC) transformation, NSC of extracted signals is transformed into the DC value. The amplitude of synthetic vector of NSC is used to evaluate the severity of stator fault. On the other hand, the extracted fundamental component can be filtered out to make the rotor fault feature emerge from the stator current spectrum. Experiment results indicate that this method is feasible and effective in both interturn short circuit and broken rotor bars fault diagnosis. Furthermore, only stator currents and voltage frequency are needed to be recorded, and this method is easy to implement

Intelligent Condition Monitoring and Prognostic Methods with Applications to Dynamic Seals in the Oil & Gas Industry

The capital-intensive oil & gas industry invests billions of dollars in equipment annually and it is important to keep the equipment in top operating condition to help maintain efficient process operations and improve the rate of return by predicting failures before incidents. Digitalization has taken over the world with advances in sensor technology, wireless communication and computational capabilities, however oil & gas industry has not taken full advantage of this despite being technology centric. Dynamic seals are a vital part of reciprocating and rotary equipment such as compressor, pumps, engines, etc. and are considered most frequently failing component. Polymeric seals are increasingly complex and non-linear in behavior and have been the research of interest since 1950s. Most of the prognostic studies on seals are physics-based and requires direct estimation of different physical parameters to assess the degradation of seals, which are often difficult to obtain during operation. Another feasible approach to predict the failure is from performance related sensor data and is termed as data-driven prognostics. The offline phase of this approach is where the performance related data from the component of interest are acquired, pre-processed and artificial intelligence tools or statistical methods are used to model the degradation of a system. The developed models are then deployed online for a real-time condition monitoring. There is a lack of research on the data-driven based tools and methods for dynamic seal prognosis. The primary goal in this dissertation is to develop offline data-driven intelligent condition monitoring and prognostic methods for two types of dynamic seals used in the oil & gas industry, to avoid fatal breakdown of rotary and reciprocating equipment. Accordingly, the interest in this dissertation lies in developing models to effectively evaluate and classify the running condition of rotary seals; assess the progression of degradation from its incipient to failure and to estimate the remaining useful life (RUL) of reciprocating seals. First, a data-driven prognostic framework is developed to classify the running condition of rotary seals. An accelerated aging and testing procedure simulating rotary seal operation in oil field is developed to capture the behavior of seals through their cycle of operation until failure. The diagnostic capability of torque, leakage and vibration signal in differentiating the health states of rotary seals using experiments are compared. Since the key features that differentiate the health condition of rotary seals are unknown, an extensive feature extraction in time and frequency domain is carried out and a wrapper-based feature selection approach is used to select relevant features, with Multilayer Perceptron neural network utilized as classification technique. The proposed approach has shown that features extracted from torque and leakage lack a better discriminating power on its own, in classifying the running condition of seals throughout its service life. The classifier built using optimal set of features from torque and leakage collectively has resulted in a high classification accuracy when compared to random forest and logistic regression, even for the data collected at a different operating condition. Second, a data-driven approach to predict the degradation process of reciprocating seals based on friction force signal using a hybrid Particle Swarm Optimization - Support Vector Machine is presented. There is little to no knowledge on the feature that reflects the degradation of reciprocating seals and on the application of SVM in predicting the future running condition of polymeric components such as seals. Controlled run-to-failure experiments are designed and performed, and data collected from a dedicated experimental set-up is used to develop the proposed approach. A degradation feature with high monotonicity is used as an indicator of seal degradation. The pseudo nearest neighbor is used to determine the essential number of inputs for forecasting the future trend. The most challenging aspect of tuning parameters in SVM is framed in terms of an optimization problem aimed at minimizing the prediction error. The results indicate the effectiveness and better accuracy of the proposed approach when compared to GA-SVM and XGBoost. Finally, a deep neural network-based approach for estimating remaining useful life of reciprocating seals, using force and leakage signals is presented. Time domain and frequency domain statistical features are extracted from the measurements. An ideal prognostic feature should be well correlated with degradation time, monotonically increasing or decreasing and robust to outliers. The identified metrics namely: monotonicity, correlation and robustness are used to evaluate the goodness of extracted features. Each of the three metric carries a relative importance in the RUL estimation and a weighted linear combination of the metrics are used to rank and select the best set of prognostic features. The redundancy in the selected features is eliminated using Kelley-Gardner-Sutcliffe penalty function-based correlation-clustering algorithm to select a representative feature from each of the clusters. Finally, RUL estimation is modeled using a deep neural network model. Run-to-failure data collected from a reciprocating set-up was used to validate this approach and the findings show that the proposed approach can improve the accuracy of RUL prediction when compared to PSO-SVM and XGBoost regression. This research has important contribution and implications to rotary and reciprocating seal domain in utilizing sensors along with machine learning algorithms in assessing the health state and prognosis of seals without any direct measurements. This research has paved the way to move from a traditional fail-and-fix to predict-and-prevent approach in maintenance of seals. The findings of this research are foundational for developing an online degradation assessment platform which can remotely monitor the performance degradation of seals and provide action recommendations on maintenance decisions. This would be of great interest to customers and oil field operators to improve equipment utilization, control maintenance cost by enabling just-in-time maintenance and increase rate of return on equipment by predicting failures before incidents

Uma abordagem neural no diagnóstico de falhas em rolamentos de motores de indução trifásicos

The three phase induction motor occupies a prominent position in the production of electromotive force and this makes it widely used in industrial applications. Consequently, it is also subjected to the conditions of operation and maintenance of the machines as a whole, as well as faults which they are subject. Thus, this paper proposes an alternative method to traditional in fault detection in bearing of induction motors connected directly to the power grid. The objectives consist in using a neural approach able to classify the existence of bearing fault with a high percentage of correct. Analyzing for this, in the time domain, one half cycle of the voltages and currents of stator the motor in study. The proposal is validated through experimental tests on a computer and monitoring on-line embedded in a DSP. As a result, the work has the creation of a database of failure, with more than a thousand trials involving the main flaws found in three phase induction motors. These tests are performed considering the conditions of voltage supply unbalanced and with several situations of mechanical load on the machine shaft.Fundação Araucária, CNPqO motor de indução trifásico ocupa uma posição de destaque na produção de força eletromotriz e isso o torna vastamente utilizado em aplicações industriais. Consequentemente, também fica submetido às condições de funcionamento e manutenção das máquinas como um todo, bem como das falhas que os mesmos estão sujeitos. Assim, este trabalho propõe um método alternativo aos tradicionais para detecção de falhas em rolamentos de motores de indução trifásicos ligados diretamente a rede elétrica. Os objetivos consistem na utilização de uma abordagem neural capaz de classificar a existência de falha de rolamento com um alto percentual de acerto. Analisando para isto, no domínio do tempo, um semiciclo das tensões de alimentação e das correntes de estator dos motor em estudo. A proposta é validada através de ensaios experimentais num computador e de forma on-line embarcada num DSP. Como conseqüência do trabalho tem-se a criação de um banco de dados de falhas, com mais de mil ensaios envolvendo as principais falhas encontradas em motores de indução trifásicos. Estes ensaios são realizados contemplando as condições de desbalanço de tensão de alimentação e com várias situações de carga mecânica no eixo da máquina

Sviluppo di una Metodologia per la Selezione e il Controllo Qualità di Ventilatori per Cappe Aspiranti in Linea di Produzione Mediante Analisi Vibrazionale

Fin dai primi anni del secolo scorso i ricercatori hanno condotto ricerche e sviluppato soluzioni per diagnosticare l’insorgere di difettosità nei motori ad induzione per aumentarne l’affidabilità e la qualità. La letteratura è ricca di esempi nei quali vengono utilizzate le più conosciute tecniche di elaborazione del segnale e negli ultimi anni l’utilizzo di algoritmi di intelligenza artificiale ha portato ad ulteriori miglioramenti nella prevenzione dei guasti e delle loro conseguenze. In questo lavoro viene presentato un approccio differente per diagnosticare la presenza di difettosità nei motori ad induzione, una metodologia originale per il tipo di applicazione basata sul calcolo delle divergenze statistiche tra distribuzioni di probabilità e sul calcolo delle entropie e della cross-entropia. Vengono proposti e confrontati cinque diversi metodi per ottenere le distribuzioni di probabilità dai segnali misurati, due differenti formulazioni per il calcolo delle divergenze e quattro per il calcolo dell’entropia. L’efficacia e la maggiore robustezza degli indicatori calcolati con il metodo proposto rispetto ai tradizionali indicatori statistici sono dimostrate tramite le analisi condotte sulle misure accelerometriche acquisite durante lo sviluppo della procedura per il controllo qualità dei ventilatori per cappe aspiranti uscenti dalla linea di produzione di SIT S.p.A. Ne viene presentata inoltre una versione modificata utilizzando la trasmissibilità del banco di collaudo come filtro inverso, soluzione che la rende efficace anche quando applicata alle misure acquisite dal sensore accelerometrico di linea. La procedura proposta ha dimostrato capacità di classificazione con un accuratezza superiore al 95%. Infine, sfruttando le potenzialità del machine learning, viene proposta una soluzione che, utilizzando un Autoencoder, è in grado di migliorare i risultati ottenuti in precedenza, raggiungendo valori analoghi come accuratezza ma migliori in termini di falsi negativi.Since the early years of the last century, researchers have conducted research and developed solutions to diagnose the onset of defects in induction motors to increase their reliability and quality. The literature is full of examples in which the well-known signal processing techniques are used and in recent years the use of artificial intelligence algorithms has led to further improvements in the prevention of faults and their consequences. In this work a different approach is presented to diagnose the presence of defects in induction motors, an original methodology for the type of application based on the calculation of statistical divergences between probability distributions and on the calculation of entropies and cross-entropy. Five different methods for obtaining probability distributions from measured signals are proposed and compared, two different formulations for calculating divergences and four for calculating entropy. The effectiveness and greater robustness of the indicators calculated with the proposed method compared to traditional statistical indicators are demonstrated through the analyses conducted on the accelerometric measurements acquired during the development of the procedure for the quality control of the fans for extractor hoods leaving the production line of SIT S.p.A. A modified version is also presented using the transmissibility of the production bench as an inverse filter, a solution that makes it effective even when applied to the measurements acquired by the accelerometric sensor positioned on the production station. The proposed procedure has demonstrated classification capabilities with an accuracy greater than 95%. Finally, exploiting the potential of machine learning, a solution is proposed which, using an Autoencoder, is able to improve the results previously obtained, reaching similar values in terms of accuracy but better in terms of false negatives

Uma nova abordagem na resoluçăo do problema do caixeiro viajante /

Orientador: Sergio ScheerCo-orientadora: Maria Teresinha Arns SteinerTese (doutorado) - Universidade Federal do Paraná, Setores de Tecnologia e Cięncias Exatas, Programa de Pós-Graduaçăo em Métodos Numéricos em Engenharia. Defesa: Curitiba, 2005Inclui bibliografi

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

The Use of Advanced Soft Computing for Machinery Condition Monitoring

The demand for cost effective, reliable and safe machinery operation requires accurate fault detection and classification. These issues are of paramount importance as potential failures of rotating and reciprocating machinery can be managed properly and avoided in some cases. Various methods have been applied to tackle these issues, but the accuracy of those methods is variable and leaves scope for improvement. This research proposes appropriate methods for fault detection and diagnosis. The main consideration of this study is use Artificial Intelligence (AI) and related mathematics approaches to build a condition monitoring (CM) system that has incremental learning capabilities to select effective diagnostic features for the fault diagnosis of a reciprocating compressor (RC). The investigation involved a series of experiments conducted on a two-stage RC at baseline condition and then with faults introduced into the intercooler, drive belt and 2nd stage discharge and suction valve respectively. In addition to this, three combined faults: discharge valve leakage combined with intercooler leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with suction valve leakage were created and simulated to test the model. The vibration data was collected from the experimental RC and processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. A large number of potential features are calculated from the time domain, the frequency domain and the envelope spectrum. Applying Neural Networks (NNs), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs) which integrate with Genetic Algorithms (GAs), and principle components analysis (PCA) which cooperates with principle components optimisation, to these features, has found that the features from envelope analysis have the most potential for differentiating various common faults in RCs. The practical results for fault detection, diagnosis and classification show that the proposed methods perform very well and accurately and can be used as effective tools for diagnosing reciprocating machinery failure