Evaluation of Bearing Fault Detection on Different _K-Folds using Deep Learning Ensemble Models

One of the most crucial parts of contemporary machinery and industrial equipment is the induction motor. Therefore, it is essential to create a fault diagnosis system that can identify induction motor problems and operating circumstances before they become serious. In this study, an induction motor's defect diagnosis is carried out in three different states, including normal, rotor fault, and bearing fault. The suggested fault diagnostic system is also described, along with a GUI. The experimental findings support the suitability of the suggested approach for rotor and bearing defects in induction motor diagnosis. A GUI for defect diagnostics was also created and used in a real-world setting. We have used Chi-Square method for high score attributes values. For the normal, rotor fault, and bearing fault states of induction motors identified by DBN, CNN, SNN, SVM and RF respectively, the fault detection system's accuracy in the actual world. In the experiment, we find Algorithms model-II, K-Folds (5, 10 & 15) , Accuracy (%), Training loss, Validation loss value for RF-SVM-CNN are 89.2, 0.260013, 0.304936 for k fold 5, 98.4, 0.155960, 0.154133 for k-fold 10 and 98.3, 0.155759, 0.144127 for k- fold 15 respectively

FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method

Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors. For this reason, performing correct training and validation of automated failure detection methods is challenging. This paper proposes a methodology called FaultFace for failure detection on Ball-Bearing joints for rotational shafts using deep learning techniques to create balanced datasets. The FaultFace methodology uses 2D representations of vibration signals denominated faceportraits obtained by time-frequency transformation techniques. From the obtained faceportraits, a Deep Convolutional Generative Adversarial Network is employed to produce new faceportraits of the nominal and failure behaviors to get a balanced dataset. A Convolutional Neural Network is trained for fault detection employing the balanced dataset. The FaultFace methodology is compared with other deep learning techniques to evaluate its performance in for fault detection with unbalanced datasets. Obtained results show that FaultFace methodology has a good performance for failure detection for unbalanced datasets

Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms

The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data

Constructing a reliable health indicator for bearings using convolutional autoencoder and continuous wavelet transform

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data‐driven methods have been proposed to construct the , most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data‐driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two‐dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the . The proposed method was tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults

Prediction of combustion state through a semi-supervised learning model and flame imaging

Accurate prediction of combustion state is crucial for an in-depth understanding of furnace performance and optimize operation conditions. Traditional data-driven approaches such as artificial neural networks and support vector machine incorporate distinct features which require prior knowledge for feature extraction and suffers poor generalization for unseen combustion states. Therefore, it is necessary to develop an advanced and accurate prediction model to resolve these limitations. This study presents a novel semi-supervised learning model integrating denoising autoencoder (DAE), generative adversarial network (GAN) and Gaussian process classifier (GPC). The DAE network is established to extract representative features of flame images and the network trained through the adversarial learning mechanism of the GAN. Structural similarity (SSIM) metric is introduced as a novel loss function to improve the feature learning ability of the DAE network. The extracted features are then fed into the GPC to predict the seen and unseen combustion states. The effectiveness of the proposed semi-supervised learning model, i.e., DAE-GAN-GPC was evaluated through 4.2 MW heavy oil-fired boiler furnace flame images captured under different combustion states. The averaged prediction accuracy of 99.83% was achieved for the seen combustion states. The new states (unseen) were predicted accurately through the proposed model by fine-tuning of GPC without retraining the DAE-GAN and averaged prediction accuracy of 98.36% was achieved for the unseen states. A comparative study was also carried out with other deep neural networks and classifiers. Results suggested that the proposed model provides better prediction accuracy and robustness capability compared to other traditional prediction models

SIMILARITY-BASED MULTI-SOURCE TRANSFER LEARNING APPROACH FOR TIME SERIES CLASSIFICATION

This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods

Diagnóstico de fallos en generadores tipo jaula de ardilla de turbinas eólicas mediante la señal de corriente

In relation to maintenance, the main strategy of the wind industry is predictive maintenance based on the constant monitoring of various types of signals obtained from the components of the wind turbines (WTs) through sensors. Since all dynamic equipment produces acoustic or ultrasound vibration, this type of signal is generally used to monitor from the blades to the tower, and most of the existing references on fault detection and diagnosis use the vibration signal. However, there is a lack of publications on other types of signals, especially when it comes to field work. Therefore, this thesis is dedicated exclusively to the study of the current signal and its application to the maintenance of the squirrel-cage induction generator used in WTs. The research includes from the historical aspects of the use of the current signal, theoretical foundations on how the components associated with faults are manifested in the signal spectrum and the methodologies for detection and diagnosis, ranging from techniques for signal processing and traditional artificial intelligence (AI) models, to deep learning models, which represent the state of the art in AI modelsEn relación con el mantenimiento, la principal estrategia de la industria eólica es el mantenimiento predictivo basado en el monitoreo constante de varios tipos de señales obtenidas de los componentes de las turbinas eólicas (TEs) mediante sensores. Como todos los equipos dinámicos producen vibración acústica o ultrasonido, este tipo de señal es la que se utiliza generalmente para monitorear desde las palas hasta la torre, y la mayoría de las referencias existentes sobre detección y diagnóstico de fallos utilizan la señal de vibración. Sin embargo, existe una carencia de publicaciones sobre otro tipo de señales, especialmente cuando se trata de trabajos de campo. Por lo expuesto, esta tesis se dedica exclusivamente al estudio de la señal de corriente y su aplicación al mantenimiento del generador de inducción tipo jaula de ardilla utilizado en TEs. La investigación incluye desde los aspectos históricos del uso de la señal de corriente, fundamentos teóricos sobre cómo se manifiestan en el espectro de la señal las componentes asociadas a fallos y las metodologías para la detección y diagnóstico, abarcando desde las técnicas para procesamiento de señales y modelos de inteligencia artificial (IA) tradicionales, hasta los modelos de aprendizaje profundo, que representan el estado del arte en modelos de IAEscuela de DoctoradoDoctorado en Ingeniería Industria