Precise Fault Diagnosis Research Articles

A CNN-based Fault Diagnosis Method of Multi-function Integrated RF System using Frequency Domain Scanning with Lasso Regression

Multi-function Integrated RF System (MIRFS) is a key unit for improving the reliability and safety of advanced aircraft. Single-frequency point data are easily affected by various interferences and noises resulting in unreliable and insufficient information. To reduce the low fault diagnosis rate caused by this effect, a novel fault diagnosis method is proposed based on frequency domain scanning and Lasso regression in this paper. Firstly, performance parameter sequences under each fault condition are extracted using frequency domain scanning. By extracting the performance parameters of a specific frequency band as raw fault data, the reliability of the data is greatly increased. Secondly, Lasso regression prioritizes the performance parameter sequences according to their correlation with faults. This approach selects the optimal performance parameter sequences as the data source for fault diagnosis, resolving the issue of excessively high dimensions in fault data. Finally, convolutional neural networks (CNNs) achieve precise fault diagnosis for both soft and hard faults. The results confirm that the proposed method overcomes the low distinguishability of fault states for both single frequency point data and sequence feature data. Compared with diagnostic models using sequence feature data as datasets, the diagnostic accuracy for hard faults increased by 7.92%, and for soft faults by 5.34%.

Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system

The efficacy of advanced deep-learning diagnostic methods is contingent mainly upon sufficient trainable data for each fault category. However, gathering ample data in real-world scenarios is often challenging, rendering these deep-learning techniques ineffective. This paper introduces a novel Prototype Matching-based Meta-Learning (PMML) approach to address the few-shot fault diagnosis under constrained data conditions. Initially, the PMML’s feature extractor is meta-trained within the Model-Agnostic Meta-Learning framework, utilizing multiple fault classification tasks from known operational conditions in the source domain to acquire prior meta-knowledge for fault diagnosis. Subsequently, the trained feature extractor is employed to derive meta-features from few-shot samples in the target domain, and metric learning is conducted to facilitate swift and precise few-shot fault diagnosis, leveraging meta-knowledge and similarity information across sample sets. Moreover, instead of utilizing all target domain samples, the prototype of each fault category is used to capture similarity information between support and query samples. Concurrently, BiLSTM is employed to selectively embed the meta-feature prototype, enabling the extraction of more distinguishable metric features for enhanced metric learning. Finally, the effectiveness of the proposed PMML is validated through a series of comparative experiments on two fault datasets, demonstrating its outstanding performance in addressing both zero-shot and few-shot fault diagnosis challenges.

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

Failure analysis of photovoltaic strings by constructing a digital multi-twin integrating theory, features, and vision

Timely and accurate failure analysis of photovoltaic (PV) systems is crucial forensuring the stable operation of power grids. However, existing failure analysis and diagnosis algorithms based on deep neural networks excessively rely on high-quality failure state data collected by sensors. This is extremely difficult to achieve in real photovoltaic power plants that are commonly equipped with self-protection mechanisms. To address this issue, we propose a Digital Multi-Twin integrating Theory, Features, and Vision (TFV-DMT) for failure analysis of PV strings in PV systems. This method first constructs theoretical simulation twins, feature twins, and visual twins based on the concept of digital twins, specifically tailored for actual PV systems, and designs a multi-twin collaborative model for model updating and failure diagnosis. Secondly, to better construct the visual twin, we introduce a Two-Dimensional Gram Angle Field Transformation Algorithm (TDGAF) to achieve targeted two-dimensional mapping of PV feature data, facilitating a more direct expression of failure characteristics. Furthermore, by constructing a Swish-activated Deep Convolutional Generative Adversarial Network (SDCGAN) to achieve balanced augmentation of mapping data, the model bias of theoretical simulation twins can be reduced. Finally, we propose a Swin-LT network that incorporates a Lightweight Dual-Channel Attention Module (LDAM) to better analyze the features of the visual twin, enabling more precise fault diagnosis. The algorithm has been validated on a real 250 kW grid-connected PV system, with results indicating that the proposed digital twin model is effective, achieving a diagnosis accuracy rate of 98.8 % for string failures.

Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer

Abstract As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.

A lightweight progressive joint transfer ensemble network inspired by the Markov process for imbalanced mechanical fault diagnosis

Owing to safety limitations and data collection costs, scenarios with imbalanced data usually arise, posing a great challenge for precise fault diagnosis. Targeting imbalanced fault diagnosis and the high computational cost of mainstream ensemble learning methods currently used, this article proposes a lightweight and accurate scheme based on a progressive joint-transfer ensemble network (PJTEN) and a Markov-lightweight strategy (MLS). Specifically, a PJTEN is developed, incorporating a multiple excitation-channel attention basic estimator and progressive joint-transfer strategy (PJTS) to maintain diversity of basic estimators better and focus more on key information from minority classes. Besides, the MLS guided by Markov transition probabilities is for the first time constructed for ensemble learning to reduce the network redundancy by alternating optimization. Using a standard dataset and a brand-new dataset of a real ship propulsion system, the proposed method achieves leading results in Accuracy, F1 score and MCC, compared with eight cutting-edge methods, thereby validating its substantial value. In terms of lightweight operation, such as temporal complexity (TC), spatial complexity (SC), and time efficiency, it is also ahead of the latest ensemble-based methods.

Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network

Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method based on time-frequency analysis and convolutional neural networks. Firstly, continuous wavelet transform (CWT) is used to convert the collected vibration signals into time-frequency diagrams, providing a comprehensive database for fault diagnosis. Next, a SqueezeNet-based fault diagnosis model is developed to identify faults. To validate the effectiveness of the proposed method, fault signals from the fluid end were collected, and fault diagnosis experiments were conducted. The experimental results demonstrated that the proposed method achieved an accuracy of 97.77% in diagnosing nine types of faults at the fluid end, effectively enabling precise fault diagnosis, which is higher than the accuracy of a 1D convolutional neural network by 14.55%. This study offers valuable insights into the fault diagnosis of drilling pumps and other complex equipment.

Squeeze‐and‐excitation attention residual learning of propulsion fault features for diagnosing autonomous underwater vehicles

AbstractGiven the demanding and unpredictable operational conditions, autonomous underwater vehicles (AUVs) often encounter different propulsion faults, leading to significant economic losses and mission impairments. To address this challenge, vibratory time‐series features can be extracted for the precise propulsion fault diagnosis of AUVs. A squeeze‐and‐excitation (SE) attention residual network (SEResNet) is therefore put forward to enhance the feature extraction for AUV propulsion fault diagnosis. By leveraging the vibratory time‐series data obtained from the AUV, an SE attention mechanism is embedded into a residual network. This integration facilitates the extraction of pertinent vibratory fault features, subsequently utilized for accurate diagnosis of any propulsion faults. The effectiveness of the proposed SEResNet was validated through its application to an actual experimental AUV, with comparison against the state‐of‐the‐arts. The results reveal that the present SEResNet outperforms all other comparison methods in terms of diagnosis performance for AUV propulsion faults.

Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Extract Deep Information of Bearing Fault in Steam Turbines via Deep Belief Network

Extracting to enhance the accuracy of diagnosing bearing faults in steam turbines, a novel approach focused on extracting key fault features from vibration signals is introduced. Recognizing the complex, non-linear, and non-stationary nature of bearing vibration signals, our strategy involves a sensitivity analysis utilizing a multivariate diagnostic algorithm. The process begins with collecting vibration data from defective bearings via the TMI system. This data is then subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), enabling the integration of adaptive noise for the extraction of in-depth information. Following this, an analysis in both time and frequency domains — post Fast Fourier Transform (FFT) — is conducted on the decomposed signals, forming the basis of a diagnostic features database. To streamline data analysis and boost the model’s computational efficiency, a combination of eXtreme Gradient Boosting (XGBoost) and Mutual Information Criterion (MIC) is applied for dimensionality reduction. Furthermore, a deep belief network (DBN) is implemented to develop a precise fault diagnosis model for the bearings in rotating machinery. By incorporating sensitivity analysis, a diagnostic matrix is crafted, facilitating highly accurate fault identification. The superiority of this diagnostic algorithm is corroborated by testing with real on-site data and a benchmark database, demonstrating its enhanced diagnostic capabilities relative to other feature selection techniques.

Wave-ConvNeXt: An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform

Wave-ConvNeXt: An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

Sparse Wasserstein stationary subspace analysis for fault detection and diagnosis of nonstationary industrial processes

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ2,p-norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster.

A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions.

Ensuring the smooth operation of rolling bearings requires a precise fault diagnosis. Particularly, identifying fault types under varying working conditions holds significant importance in practical engineering. Thus, we propose a reinforcement ensemble method for diagnosing rolling bearing faults under varying working conditions. Firstly, a reinforcement model was designed to select the optimal base learner. Stratified random sampling was used to extract four datasets from raw training data. The reinforcement model was trained by these four datasets, respectively, and we obtained four optimal base learners. Then, a sparse ANN was designed as the ensemble model and the reinforcement learning model that can successfully identify the fault type under variable work conditions was constructed. Extensive experiments were conducted, and the results demonstrate the superiority of the proposed method over other intelligent approaches, with significant practical engineering benefits.

The acoustic-vibration characteristics of cylindrical roller bearings with raceway failures: Simulation and experiment

This paper presents a mathematical model for cylindrical roller bearings, the precision noise test and simulation investigation are employed, the discussion focuses on the systematic investigation of the vibration characteristics, noise properties, and noise transmission attributes of defective bearings. The findings indicate that severe displacement oscillations of rollers and oil film oscillations can be observed due to the raceway fault, the average slip rate of rollers is seriously affected, resulting in a reduction in the operational stability of the cage. The noise shock is also introduced, which is primarily reflected in the mid-frequency range (670–8000 Hz) of the spectrum, leading to a deviation from the normal distribution of noise data points. This characteristic makes it challenging to detect noticeable shock signals when testing bearing noise at a distance. This research holds the promise of deepening comprehension of faulty bearing dynamics, thereby furnishing richer means for precise fault diagnosis.

Identification and Fault Diagnosis of Rolling Element Bearings Using Dimension Theory and Machine Learning Techniques

Abstract The study presents the classification of bearing fault types occurring in rotating machines using machine learning techniques. Recent condition monitoring demands all-inclusive but precise fault diagnosis for industrial machines. The utilization of mathematical modeling with machine learning may be combined for fine fault diagnosis under different working conditions. The current study presents a blend of dimensional analysis (DA) and a K-nearest neighbor (KNN) to diagnose faults in industrial roller bearings. Vibrational responses are collected for several industrial machines under diverse operational conditions. Bearing faults are identified using the DA model with 3.62% error (avg) and classified using KNN with 98.67% accuracy. Comparing the performance of models with experimental and artificial neural networks (ANN) validated the potential of the current approach. The results showed that the KNN demonstrates superior performance in terms of feature prediction and extraction of industrial bearing.

A fault diagnosis method for flexible converter valve equipment based on DSC-BIGRU-MA

Introduction: Precise fault diagnosis is crucial for enhancing the reliability and lifespan of the flexible converter valve equipment. To address this issue, depthwise separable convolution, bidirectional gate recurrent unit, and multi-head attention module (DSC-BiGRU-MAM) based fault diagnosis approach is proposed.Methods: By DSC and BiGRU operation, the model can capture the correlation between local features and temporal information when processing sequence data, thereby enhancing the representation ability and predictive performance of the model for complex sequential data. In addition, by incorporating a multi-head attention module, the proposed method dynamically learns important information from different time intervals and channels. The proposed MAM continuously stimulates fault features in both time and channel dimensions during training, while suppressing fault independent expressions. As a result, it has made an important contribution to improving the performance of the fault diagnosis model.Results and Discussion: Experimental results demonstrate that the proposed method achieves higher accuracy compared to existing methods, with an average accuracy of 95.45%, average precision of 88.67%, and average recall of 89.03%. Additionally, the proposed method has a moderate number of model parameters (17,626) and training time (935 s). Results indicate that the proposed method accurately diagnoses faults in flexible converter valve equipment, especially in real-world situations with noise overlapping signals.

A Bearing Fault Diagnosis Method Based on Dilated Convolution and Multi-Head Self-Attention Mechanism

Rolling bearings serve as the fundamental components of rotating machinery. Failure to detect damage early in these components can result in equipment shutdown, leading not only to economic losses but also to a threat to worker safety. Given the diverse range of rotating parts, it is crucial to promptly identify and accurately diagnose early bearing failures during the maintenance of large-scale machinery. To achieve quick and precise fault diagnosis, this study proposes a method based on dilated convolution, a Bidirectional Gated Recurrent Unit (BiGRU), and a multi-head self-attention mechanism. The key advantage lies in its ability to directly process raw 1D sampled data without requiring complex time–frequency domain conversion. To validate the model’s accuracy and stability, we conducted empirical studies using both the HUST bearing dataset proposed by Thuan, Nguyen et al. and the CWRU bearing dataset from Western Reserve University. The results demonstrate that our model achieves an impressive accuracy rate of 99.94%, along with an f1 value for the test set when dealing with multiple operating conditions for all five types of bearings in the HUST dataset. Moreover, when applied to the CWRU dataset, these two metrics even reached 99.95%. Furthermore, the proposed model achieves a significant prediction accuracy of more than 98.5% on two datasets containing different types of noise and different levels of white Gaussian noise, highlighting its great potential in practical applications of early bearing fault diagnosis.

Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems.

Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model

As a crucial component in the transmission system, a planetary gearbox has a relatively complicated structure and usually operates under complex working conditions and a severe noisy environment, making it challenging to achieve precise and efficient fault diagnosis. Along with the development of artificial intelligence techniques, end-to-end fault diagnosis frameworks have been widely studied, among which convolutional and recurrent neural networks are the mainstream backbone networks. However, these networks have shortcomings in computational efficiency and feature extraction, which lead to the application of a self-attention mechanism. This paper presents a fault diagnosis method based on frequency domain Gramian angular field (GAF) and Markov transition field (MTF) features for planetary gearboxes by combining the characteristics of vibration signal fault diagnosis and transformer network structure. The experiments show that the frequency domain GAF-MTF features can effectively reduce the influence of time shifting between samples and improve diagnostic accuracy. Furthermore, comparisons with other mainstream models indicate that the proposed method can obtain competitive results and achieve more accurate and robust performance under noisy conditions.