Vibration-based Fault Diagnostics in Wind Turbine Gearboxes Using Machine Learning

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal components unrelated to faults and high levels of noise obscure the signature generated by early-stage damage. To address this issue, we propose an innovative fault diagnosis framework that utilizes deep learning and leverages cyclostationary analysis of sensor data. By generating cyclic spectral coherence maps from the sensor data, we can emphasize fault-related signatures. These 2D color map representations are then used to train convolutional neural networks capable of detecting even minor faults and early-stage damages. The proposed method is evaluated using test data obtained from multibody dynamic simulations conducted under various operating conditions. The benchmark test cases, inspired by an NREL study, are successfully detected using our approach. To further enhance the accuracy of the model, subsequent studies employ Convolutional Neural Networks with Local Interpretable Model-Agnostic Explanations (LIME). This approach aids in interpreting classifier predictions and developing an interpretable classifier by focusing on a subset range of cyclic spectral coherence maps that carry the unique fault signatures. This improvement contributes to better accuracy, especially in scenarios involving multiple faults in the gearbox that need to be identified. Moreover, to address the challenge of applying this framework in practical settings, where standard deep learning techniques tend to provide inaccurate predictions for unseen faults or unusual operating conditions, we investigate fault diagnostics using a Bayesian convolutional neural network. This approach incorporates uncertainty bounds into prediction results, reducing overconfident misclassifications. The results demonstrate the effectiveness of the Bayesian approach in fault diagnosis, offering valuable implications for condition monitoring in other rotating machinery applications

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

딥러닝 기반 와류기인 선박 프로펠러 진동 탐지 기술

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부(멀티스케일 기계설계전공), 2022. 8. 김윤영.국제해사기구(IMO)의 탄소 배출량 저감 규제 등의 규제에 따라 조선 해운업계는 선박의 초대형화와 에너지 저감장치(ESD) 등 친환경 장치 적용으로 대응하고 있다. 이에 따라 선박의 프로펠러, 러더, ESD 등 수중 구조물의 설계 변화가 요구되고 있다. 새로운 설계 요구조건에 맞춰 주요 제원이 결정되며 전산유체해석 및 수조시험을 통한 성능설계, 진동해석 및 구조강도해석을 통한 구조설계가 진행된다. 수중구조물 제작 이후에는 품질검사를 거쳐 시운전 중에 성능과 진동평가를 마치면 선박이 인도된다. 친환경 장치가 설치된 대형 상선의 선미 구조물은 형상이 복잡하여 유동 및 진동특성의 설계 민감도가 크고 생산 공차에 따른 피로수명의 산포가 크기 때문에 초기 설계단계에서 모든 품질문제를 걸러 내기 어려운 문제가 있다. 특히 유동장에 있는 수중구조물의 경우 특정 유속에서 와류 이탈이 발생하게 되며 와류 이탈 주파수가 구조물의 고유진동수가 일치하는 경우 공진에 인한 와류기인진동(Vortex Induced Vibration; VIV) 문제가 종종 발생되어 수중구조물 피로손상의 원인이 되고 있다. VIV 문제가 있는 상태로 선박이 인도될 경우 설계수명을 만족하지 못하고 단기간에 파손이 되는 경우가 많아 조선소에 큰 피해를 주기 때문에 선박 인도 직전인 선박 시운전 단계에서 진동이나 응력 계측을 통해 VIV 발생 여부의 확인이 필요하다. 구조물에 작용하는 하중을 계측하는 전통적인 방법은 구조물에 스트레인게이지를 설치하고 수중 텔레미터리를 설치하여 구조물의 스트레인을 직접 계측하는 방법이지만 계측을 위해 많은 비용이 소요되고 계측 실패의 가능성이 매우 높다는 문제가 있다. 본 논문에서는 대형 상선 프로펠러의 대표적인 손상 원인인 Vortex Induced Vibration을 시운전 단계에서 선체 진동 계측을 통해 간접적으로 검출할 수 있는 방법을 제안하였다. 특정 VIV가 문제가 되는 경우는 유속에서 와류 이탈 주파수가 구조물의 고유진동수가 일치하는 경우 공진에 의해 와류이탈 강도가 증가하고 유속이 증가하더라도 와류이탈 주파수가 유지되는 Lock-in 현상이 발생하는 경우로 간접 계측을 통해 이를 명시적으로 확인할 수 있다. 이를 위해서는 진동 전문가의 반복적인 진동 계측 및 평가 프로세스가 필요한데 본 연구에서는 전문가를 대신한 딥러닝 알고리즘을 이용한 VIV 탐지 시스템을 제안하였다. 진동 분석과 VIV 검출 자동화를 위해 이미지 기반의 Object detection을 위해 널리 이용되고 있는 CNN(Convolution Neural Network) 알고리즘을 이용하였다. 본 연구에서는 Object detection을 수행하되 Classification은 수행하지 않아도 되는 특징이 있어 이에 특화된 CNN 모델 개발을 위해 Hyper parameter를 조정하여 Hidden Layer를 증가하는 방법으로 30개의 CNN모델을 검토하였고 최종적으로 과적합이 없이 탐지 성능이 높은 5개의 Hidden layer 가진 모델을 제안하였다. CNN 학습을 위해 필요한 대규모의 데이터 생성을 위해 진동 모드 중첩법 기반의 간이 선박 모델을 제안하였고 프로펠러 기진력을 모사하였다. 간이 모델을 이용하여 실제 진동계측 결과와 유사한 진동 특성을 보이는 10,000개의 데이터를 생성하여 학습에 이용하였고 1,000개의 데이터를 이용하여 테스트한 결과 82%이상의 탐지 성공률을 보였다. 제안된 탐지시스템의 검증을 위해 축소모델 시험을 수행하였다. 프로펠러에서 Vortex shedding 주파수와 블레이드의 수중 고유진동수가 일치하도록 설계된 1/10 스케일의 선박 추진 시스템 축소 모델을 이용하여 프로펠러에서 Vortex Induced Vibration을 발생시키고 프로펠러 주변 구조물에서 가속도계를 이용하여 Lock-in 현상에 의한 진동을 측정하였다. 이 신호를 이용하여 개발된 시스템으로 VIV의 검출이 가능함을 보였다. 마지막으로 VIV문제가 발생했던 원유운반선의 시운전 중 기관실 내에서 계측된 선체 구조 진동값을 이용하여 개발된 탐지 시스템의 타당성을 검증하고 실제 선박에서의 적용 가능성도 확인하였다. 개발된 시스템은 VIV 검출은 위한 자동화 시스템으로 활용이 가능할 것으로 보이며 향후 실선 데이터가 확보될 경우 유용성이 증가할 것으로 기대된다Due to the International Maritime Organization’s (IMO) regulations on carbon emission reduction, the shipbuilding and shipping industry increases the size of ships and adopts energy-saving devices (ESD) on ships. Accordingly, design changes of underwater structures such as propellers, rudders, and ESD of ships are required in line with these trends. The lock-in phenomenon caused by vortex-induced vibration (VIV) is a potential cause of vibration fatigue and singing of the propellers of large merchant ships. The VIV occurs when the vibration frequency of a structure immersed in a fluid is locked in its resonance frequencies within a flow speed range. Here, a deep learning-based algorithm is proposed for early detection of the VIV phenomenon. A salient feature in this approach is that the vibrations of a hull structure are used instead of the vibrations of its propeller, implying that indirect hull structure data relatively easy to acquire are utilized. The RPM-frequency representations of the measured vibration signals, which stack the vibration frequency spectrum respective to the propeller RPMs, are used in the algorithm. The resulting waterfall charts, which look like two-dimensional image data, are fed into the proposed convolutional neural network architecture. To generate a large data set needed for the network training, we propose to synthetically produce vibration data using the modal superposition method without computationally-expensive fluid-structure interaction analysis. This way, we generated 100,000 data sets for training, 1,000 sets for hyper-parameter tuning, and 1,000 data sets for the test. The trained network was found to have a success rate of 82% for the test set. We collected vibration data in our laboratory's small-scale ship propulsion system to test the proposed VIV detection algorithm in a more realistic environment. The system was so designed that the vortex shedding frequency and the underwater natural frequency match each other. The proposed VIV detection algorithm was applied to the vibration data collected from the small-scale system. The system was operated in the air and found to be sufficiently reliable. Finally, the proposed algorithm applied to the collected vibration data from the hull structure of a commercial full-scale crude oil carrier in her sea trial operation detected the propeller singing phenomenon correctly.CHAPTER 1. INTRODUCTION 1 1.1 Motivation 1 1.2 Research objectives 8 1.3 Outline of thesis 9 CHAPTER 2. PROPELLER VORTEX-INDUCED VIBRATION MEASUREMNT METHOD 24 2.1 Structural vibration measurement methods 24 2.2 Direct measuremt method for propeller vibration 26 2.3 Indirect measuremt method for propeller vibration 28 CHAPTER 3. DEEP LEARNING NETWORK FOR VIV IDENTIFICATION 39 3.1 Convolution Neural Network 39 3.2 Data generation using mode superposition 46 3.3 Structure of the proposed CNN model 50 3.4 Deep neural networks 53 3.5 Training and diagnosis steps 55 3.6 Performance of the diagnositc model 56 CHAPTER 4. EXPERIMETS AND RESULTS 76 4.1 Experimental apparatus and data collection 76 4.2 Results and discussion 78 CHAPTER 5. ENHANCEMENT OF DETECTION PERFORMANCE USING MULTI-CHANNEL APPROACH 98 CHAPTER 6. VORTEX-INDUCED VIBRATIOIN IDENTIFICATION IN THE PROPELLER OF A CRUDE OIL CARRIER 105 CHAPTER 7. CONCLUSION 114 REFERENCES 118 ABSTRACT(KOREAN) 127박

An intelligent fault diagnosis method of rotating machinery based on deep neural networks and time-frequency analysis

As the crucial part of the health management and condition monitoring of mechanical equipment, the fault diagnosis and pattern recognition using vibration signal are essential researching contents. The time-frequency representation method cannot identify the fault patterns from time-frequency representation effectively because of the complex work conditions of rotating machinery parts and the interference of strong background noise. Considering these disadvantages, a new reliable and effective method based on the time-frequency representation and deep convolutional neural networks is presented. In this method, the time-frequency features are calculated by the short time Fourier transform (STFT), and the pseudo-color map as the new identification objects. A novel feature learning method based on the sparse autoencode with linear decode is used to extract these time-frequency features, which is an unsupervised feature learning method with the goal of minimizing the loss function. The convoluting and pooling are applied to establish the hierarchical deep convolutional neural networks and filter the useful features layer by layer from the output of sparse autoencode. And a softmax classifier is used to obtain the faults classification. The experimental datasets from roller bearing and gearbox have been taken to verify the reliability and effectiveness of the proposed method for fault diagnosis and pattern recognition. The results show that the proposed method have excellent performance of the recognized objects

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