Learning Approach For Fault Diagnosis Research Articles

Sample-Evaluation-Enhanced Machine Learning Approach for Fault Diagnosis of Hybrid Systems

Sample-Evaluation-Enhanced Machine Learning Approach for Fault Diagnosis of Hybrid Systems

An Adaptive Activation Transfer Learning Approach for Fault Diagnosis

For industrial scenarios with changing operating conditions, the vibration data of different operating conditions often have different data distributions. In this article, to make the deep learning framework perform more flexible nonlinear transformations for different input data, a new activation function, i.e., parameter-free adaptively Swish (PASwish), is developed. PASwish formulates different activation schemes for different input data so that vibration data under different operating conditions can carry out adaptive nonlinear transformation, and the generalization ability of the whole network is improved. In addition, this article proposes deep parameter-free cosine networks with PASwish on the basis of PASwish, which can help adjust the network weights of domain-specific features and domain-invariant features by constructing an attention module based on cosine adjustment. Finally, the reconstruction-based domain adaptation method is used to achieve cross-domain fault diagnosis. Experiments are carried out on the bearing fault experimental platform to verify the effectiveness and generalization of the proposed method. We have achieved 95.16 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \pm 1.76\%$</tex-math></inline-formula> ) average accuracy on 72 transfer tasks, which shows better performance than current studies.

Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle

The integration of Industry 4.0 concepts into today’s manufacturing settings has introduced new technology tools that have already started providing companies an increased level of efficiency in certain operations. Autonomous Transfer Vehicles (ATV) are one of these new tools that are popular in today’s manufacturing settings. As these tools become an integral part of the manufacturing ecosystem, accurate diagnosis of ATV faults and anomalies will also be crucial in manufacturing settings. Similar to any other intelligent detection of machinery faults, analyzing and utilizing signals measured from attached ATV sensors may reveal any uncovered operational faults or critical operational/safety concerns. In this context, this paper focuses on an intelligent fault detection of an ATV tool utilizing signals measured from multiple attached sensors. A novel Convolutional Neural Network-based data fusion approach, utilizing short time Fourier Transform, is proposed for the detection and identification of operational faults occurring in an ATV. The approach is tested on an experimental dataset, consisting of two motors’ sound and vibration signals, collected as an ATV operates for a specific task under three different conditions. The diagnosis results indicate that the proposed deep learning-based multisensory fault diagnosis approach is able to diagnose operational conditions with significantly high accuracy compared to single or dual sensor approaches.

An Ensemble Learning Approach for Fault Diagnosis in Self-Organizing Heterogeneous Networks

Self-organizing networks (SON) aim to offer high quality services while reducing both capital and operational expenditures by enabling three main functions: self-configuration, self-optimization, and self-healing. Though there exits only few studies on self-healing, it plays an important role in intelligent network maintaining. In this paper, we firstly propose an ensemble learning based fault diagnosis system for self-organizing heterogeneous networks. Specifically, the base learner is strengthened in each iteration and the final diagnosis result is obtained from the combination of all base classifications for performance improvement. Moreover, traditional classification algorithms are designed with the premise of balanced data set. However, when they are applied to fault diagnosis in cellular network which has imbalanced data, the classification accuracy for minority classes is not satisfying. To address this issue, synthetic minority over-sampling technique (SMOTE) is applied to handle the data-imbalance. Furthermore, considering the fact that misclassification is unavoidable, and most existing schemes aim to achieve a low detection error rate, while ignoring the fact that different type of misclassification errors can cause different economic losses to the operators. We consider the cost sensitivity and use rescaling method to help the classifier differentiate the importance of different samples, so that a minimal total loss can be achieved. Also, for handling the sparse data and dense deployment issues in small cells of a heterogeneous network, we provide a distributed diagnose system for lowering the communications cost. Extensive simulations are performed and the results show the effectiveness of the proposed system.

An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems

Abstract The machine fault diagnosis is being considered in a larger-scale complex system with numerous measurements from diverse subsystems or components, where the collected data is with disparate characteristics and needs more prevailing methods for data preprocessing, feature extraction and selection. This work presents a novel diagnosis framework that combines the spatiotemporal pattern network (STPN) approach with convolutional neural networks (CNN) to build a hybrid ST-CNN scheme. The proposed framework is tested on two data sets for diagnosing unseen operating conditions and fault severities respectively, to evaluate its generalization ability, which is essential for the application in machine fault diagnosis as not all of the aforementioned scenarios have sufficient labeled data to train a model. The results show that the proposed ST-CNN framework outperforms or is comparable to shallow methods (support vector machine and random forest) and 1D CNN. Through visualizing the activations, it is verified that the spatial features can elevate the diagnosis accuracy, and more general features are determined by the proposed approach to form an adaptive classifier for diverse operating conditions and different fault severities.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions

Traditional machine learning algorithms have made great achievements in data-driven fault diagnosis. However, they assume that all the data must be in the same working condition and have the same distribution and feature space. They are not applicable for real-world working conditions, which often change with time, so the data are hard to obtain. In order to utilize data in different working conditions to improve the performance, this paper presents a transfer learning approach for fault diagnosis with neural networks. First, it learns characteristics from massive source data and adjusts the parameters of neural networks accordingly. Second, the structure of neural networks alters for the change of data distribution. In the same time, some parameters are transferred from source task to target task. Finally, the new model is trained by a small amount of target data in another working condition. The Case Western Reserve University bearing data set is used to validate the performance of the proposed transfer learning approach. Experimental results show that the proposed transfer learning approach can improve the classification accuracy and reduce the training time comparing with the conventional neural network method when there are only a small amount of target data.