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We focus on an estimation method based on deep learning in terms of fault correction time for the operation reliability assessment of open-source software (OSS) under the environment of an edge computing service. Then, we discuss fault severity levels in order to consider the difficulty of fault correction. We use a deep feedforward neural network in order to estimate fault correction times. In particular, we consider the characteristics of fault trends by using three-dimensional graphs. Therefore, we can increase the recognizability of the proposed method based on deep learning for large-scale fault data from the standpoint of fault severity levels under edge OSS operation.

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.

Bearing vibration characteristic analysis based on dynamic analytical model is of importance for identifying faults of bearing in mechanical systems. However, on one hand, the relationship between the raceway-ball contact pairs in bearing dynamic models are often considered as a simplified cascade; on the other hand, the evolving law of bearing fault characteristics of the early fault (wear) and severe fault (pitting) has not been fully revealed, which confines the development of bearing fault feature extraction and severity assessment methods. This paper, therefore, constructs a dynamic analytical model considering time-varying contact pairs and parameterized defects of deep groove ball bearings (DGBB) to illustrate the dynamic vibration characteristics of DGBB under different health conditions. The degrees of freedom of balls and flexible cage, multi-parameter defect excitation, mixed-elastohydrodynamic lubrication (mix-EHL), and relative slippage are all considered in the proposed model. Positions of multiple rigid bodies and contact points are dynamically expressed by coordinate systems, from which system physical parameters and geometrical relationship can be obtained. The bearing early and severe fault excitations are described by multi-parameters. The early fault which is embodied as surface roughness yields the indirect displacement excitation and the severe fault yields the direct displacement excitation. The surface roughness and indirect displacement excitation affect the mix-EHL, the radial force and friction force of contact pairs, thereby affecting the vibration response of DGBB. The evolving mechanism analysis of bearing fault characteristics are then revealed based on the established dynamic model, indicating the coupling and nonlinear relationship between the characteristic frequency amplitude and the fault severity of DGBB.

Due to the complex mechanical structure of the spring-operated mechanism, its failure mechanisms often exhibit a multi-faceted nature, involving various potential failure sources. Therefore, conducting a failure mechanism analysis for multi-source faults in such systems is essential. This study focuses on the design of composite faults in combination operating mechanisms and develops simulation scenarios with varying levels of fault severity. Given the challenges of traditional simulation methods in performing quantitative analysis of core jamming faults and the susceptibility of the core’s motion trajectory to external interference, this paper innovatively installs a spring-damping device at the extended core position. This ensures that, during the simulation of core jamming faults, the motion trajectory remains stable and unaffected by external factors, while also enabling precise control over the degree of jamming. As a result, the simulation more accurately reflects real fault conditions, thereby enhancing the accuracy and practicality of diagnostic model outcomes. This study employs the Morlet wavelet transform to convert the current and displacement signals in the time series into time-frequency spectrograms. These spectrograms are then processed using the ResNet50 deep residual neural network for feature extraction and fault classification. The results demonstrate that, when addressing the diagnostic problem of small-sample data for operating mechanism faults, ResNet50, with its residual structure design, exhibits significant advantages. The convolutional layer strategy, which first performs dimensionality reduction followed by dimensionality expansion, combined with the use of the ReLU activation function, contributes to superior performance. This approach achieves a fault recognition accuracy of up to 91.67%.

A hybrid methodology for bearing fault and severity analysis using param, eter-optimized variational mode decomposition (VMD) and deep learning (DL) algorithms is presented in this study. Different localized defects are artificially seeded at the contacting surfaces of a self-aligning bearings, and vibration data is generated (Case study II) under various radial-load and speed conditions for DL algorithm development. This data is pre-processed using the parameter-optimized VMD, where the intrinsic mode function with the highest kurtosis is processed using a deep learning (DL) algorithm. Particle swarm optimization is used for optimizing two VMD parameters. Further, seven different DL algorithms are implemented to classify various fault severity of rolling-element bearings. These algorithms are validated against the standard repository dataset of Case Western Reserve University (Case study I). The reliability of these algorithms is also tested using the generated dataset (Case study II) and the results show that 1D-CNN, WaveNet and gated recurrent unit have outperformed all other algorithms by achieving accuracies of 99.65%, 97.05% and 97.33%, respectively.

Accurately identifying the fault type of an optical current transformer (optical CT) and evaluating the fault severity can provide strong support for the operation and maintenance of a direct current (DC) power system. In response to the problems that current research overlooks, the spatiotemporal features when making fault identification, which restrain the improvement of identification accuracy, and consider fault identification as an assessment of fault severity, which is unable to provide effective information for actual operation and maintenance work, this paper proposes an optical CT fault severity assessment model based on scene generation and spatiotemporal feature fusion. Firstly, a CNN-Transformer model is constructed to mine the fault characteristics in spatial and temporal dimensions by feature fusion, achieving accurate identification of fault types. Secondly, an improved synthetic minority oversampling method is adopted to generate virtual operating scenes, and the operating range under different operating states of the optical CT is statistically obtained. Finally, based on Shapley Additive Explanations (SHAP), the importance of the feature of optical CT is evaluated under different fault types. Reliant on the importance of features and operating range under different running states, the severity of the fault is assessed by quantifying the difference between the fault state and the normal state of the optical CT under the identified fault type. This study validated the effectiveness of the proposed method using actual operational data from an optical CT at a converter station in Hebei Province in China.

Predictive maintenance contributes to Industry 4.0, as it enables a decrease in maintenance costs and downtime while aiming to increase production and return on investment. Despite the increasing utilization of machine learning techniques in predictive maintenance in industrial systems over the past few years, several challenges remain to be addressed in the implementation of ML, including the quality of the data collected, resource constraints, and equipment heterogeneity. This study proposes an adaptive framework for predictive maintenance in the context of Industry 4.0, specifically in internet of things (IoT) systems, using machine learning (ML) models. In particular, this study introduces PdM-FSA, a new framework based on an ensemble classifier that takes advantage of four widely adopted ML models in the predictive maintenance literature: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The performance evaluation results showed that the PdM-FSA framework can perform well for predictive maintenance according to the severity of equipment malfunctions in a smart factory. The results of this study provide significant knowledge to researchers and practitioners on predictive maintenance in the context of Industry 4.0. and enables the optimization of processes and improves productivity.

The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy.

This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.