Equipment Fault Research Articles

An All-Fiber FLRD System for SO2 Detection Based on Graphene-Coated Microfiber

The accurate and effective detection of SF6 decomposition components inside a gas-insulated switchgear (GIS) is crucial for equipment fault diagnosis and condition assessment. The current method for detecting SF6 decomposition components involves gas extraction at the GIS inlet, which only provides limited information on the decomposition component content. Therefore, there is a need to explore more effective ways to obtain internal gas component information within GIS. In this study, we propose a graphene-coated microfiber gas detection method for SO2. We establish a physical simulation model of the microfiber and analyze the sensing mechanism of the microfiber diameter and cladding refractive index changes in its evanescent field. A graphene-coated microfiber gas sensor was prepared using a drop-coating method, and a fiber loop ring-down (FLRD) gas detection system was constructed for the experimental studies on SO2 gas detection. The results demonstrated that the graphene-coated microfiber exhibits an excellent gas-sensitive response to SO2 and achieves trace-level detection at room temperature. The concentration range of 0 to 200 ppm showed good linearity, with a maximum detection error of 4.76% and a sensitivity of 1.24 ns/ppm for SO2. This study introduces an all-fiber method for detecting SF6 decomposition components, offering a new approach for online monitoring of SF6 decomposition components in GIS equipment using built-in fiber-optic sensors.

The Reform of Equipment Fault Class Course Based on Action-Oriented

This paper adapts to the needs of teaching, insists on the problem-oriented according to the reality of the fault course, points out the gap between the current failure course and the actual combat requirement, and takes the goal-oriented as the traction, and puts forward the basic strategy of organizing the fault course teaching according to the student's psychological development course, which can provide reference for the follow-up development of the fault course.

Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied.

Application of Digital Technology in Military Equipment Management

The traditional military equipment management mode is difficult to meet the needs of today's rapid development, and digital application has become a necessary trend to improve the efficiency and quality of equipment management. At present, the mainstream application direction of digital technology in military equipment management mainly includes equipment fault monitoring, equipment maintenance, and data analysis and optimization. However, although the application of digital technology brings many benefits, the development of digital technology faces a series of difficulties and key problems. For the military, accelerating digital transformation, overcoming the practical challenges of digital technology landing in the military, and improving the application level of digital technology will become an important development direction in military equipment management in the future.

Mono-trend mode decomposition for robust feature extraction from vibration signals of rotating Machinery

The analysis of vibration signals of rotating machinery plays an important role in equipment fault diagnosis and health monitoring. However, the decomposition of fault characteristic components often lacks robustness and accuracy under nonstationary operating conditions due to potential strong background noise and the complexity of existing components. In this paper, we address this issue by utilizing the relationships among components of vibration signals generated by rotating structures, described as the mono-trend modes (MTMs). Our proposed method, called the MTM decomposition (MTMD), enables robust feature extraction from vibration signals under nonstationary operating conditions. Initially, the instantaneous angular speed (IAS) of the input shaft is measured by tachometers or estimated by the framework of parameterized resampling time–frequency transform. The instantaneous frequencies (IFs) of the targeted components are then determined based on their orders relative to the IAS. Subsequently, the instantaneous amplitudes (IAs) of the targeted components are estimated by solving a joint optimization problem that includes a penalty term designed in consideration of the low variation characteristic of IAs. This penalty term enhances the efficient distribution of energy among close-spaced components. As a result, all targeted MTMs can be extracted simultaneously without any accumulated error. Various simulated experiments and practical experiments on a high-power planetary gearbox are conducted to analyze and validate the effectiveness of the proposed MTMD method in extracting robust features, especially in the presence of strong noise and close-spaced components.

A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model’s accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models.

Distributionally Robust Collaborative Dispatch of Integrated Energy Systems with DNE Limits Considering Renewable and Contingency Uncertainties

Collaborative optimisation of system reserves and utilisation of renewable energy is an efficient approach to achieving robust optimal dispatch of integrated energy systems (IES). However, conventional robust dispatch methods are often too conservative and lack the ability to consider uncertainties such as renewable energy and contingency probabilities. To address these limitations, this paper proposes a distributionally robust dispatch model that co-optimises reserves and do-not-exceed (DNE) limits while considering these uncertainties. First, a deterministic optimisation model of IES is established with a minimum operational cost objective and security constraints. Next, a two-stage robust collaborative optimisation framework of IES is built, based on the Wasserstein measure, with random equipment faults represented by an adjustable ambiguity set. Finally, to overcome the computational challenges associated with robust approaches, duality theory and Karush-Kuhn-Tucker (KKT) conditions are used to convert the formulation into a mixed integer linear programming (MILP) model. The Simulation results on the modified IEEE 33-bus system demonstrate the effectiveness of the proposed model and solution methodology.

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

Research on inspection of machine room by mobile robot

The machine room inspection robot, as a special robot, can locate and navigate in the machine room independently, and carry sensors to detect equipment faults and record instrument data instead of the machine room staff. In this paper, the relevant standards and existing inspection robots at home and abroad were investigated and the overall scheme was determined based on the actual machine room environment. The monitoring background employs B/S architecture, allowing staff to deliver inspection tasks and manage robots from any computer. The motion map is created using the GMapping algorithm, and the topology map for inspection is created based on the inspection task and feasible path. For raster map localization, the adaptive Monte Carlo localization algorithm is used. The functional layer software for the robot is designed based on the inspection function to be performed by the robot and the man-machine interaction with the monitoring background. The findings of this paper's research indicate that the inspection robot system can meet the requirements of computer room autonomous inspection.

BERT‐TriF: An inductive short text classification model for power equipment defect records

AbstractThe descriptions of power equipment defect records are often characterized by colloquial short texts. Standardized classification of a large number of colloquial defect descriptions has laid a solid foundation for building a power equipment knowledge graph and improving the level of intelligence in the field of power inspection. Using deep learning and natural language processing technology, this paper proposes a text classification model for power equipment defect records named Bidirectional Encoder Representations from Transformers with Family Feature Fusion (BERT‐TriF). The model firstly leverages BERT to semantically represent the input text. To extract family history as well as text implicit information, we creatively propose a family feature fusion algorithm for training. An improved multi‐head attention mechanism is developed subsequently to enhance text semantic category features and strengthen the learning ability of the model. By comparing BERT‐TriF and baseline models such as TextCNN, TextRNN, and fastText on the specified and generic text dataset, the experimental results demonstrate that it has better performance, robustness, and universality for short text classification.

Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext.

This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network's capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method's feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness.

Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model

<p>Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes.</p>

The named entity recognition of vessel power equipment fault using the multi-details embedding model

The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields.

AFDX network equipment fault diagnosis technology

This paper focuses on the network equipment fault monitoring and diagnosis software, and studies the fault diagnosis of the monitored AFDX network based on the network algorithm. Firstly, the range fault characteristic parameters are designed to identify the fault type, and the correlation between the detection results and the fault characteristic parameters at each location can be obtained. Secondly, the data storage management scheme of the first level filtering and the second level caching mechanism is designed for the data collected in the detection. Then, according to the designed fault classification, fault diagnosis methods are given respectively, and the occasional anomalies are identified and suppressed. Finally, the network fault diagnosis verification module is designed, and the experimental verification is carried out from the perspectives of real-time and concurrency. The verification results prove the effectiveness of the method.

Deep Learning-Based Composite Fault Diagnosis

As the core component of many large machinery, rotating equipment occupies a high proportion in industrial manufacturing. Intelligent fault diagnosis for them is becoming the core focus of Industry 4.0 and its evolution. To address the problem of poor fault classification accuracy of rotating equipment, this paper proposes a new solution to deploy a composite fault diagnosis model at the front end to achieve low power consumption, continuous automatic identification and monitoring of equipment faults. We creatively combined the idea of speech recognition to solve the problem that traditional feature extraction methods are insufficient in handling composite fault diagnosis when dealing with fault waveform files, and used Fbank speech feature extraction idea to extract fault features, thus simplifying the complex fault diagnosis problem into an image classification problem. The generated composite fault Fbank feature data set is significantly different in visual observation. Therefore, we further designed the LeNet-F network based on the LeNet network for composite fault diagnosis. The model was validated on the K210 development board to achieve an accuracy of 97.41% for hybrid fault classification at a model size of 2.4MB. This makes it possible to apply the deep learning method to the front end to directly complete the composite fault diagnosis.

Recovery of military communication equipment with combat damage

During hostilities, military communications equipment can be damaged to varying degrees. This is also possible in the event of accidental damage during field exercises or in the event of a violation of conservation technology in the process of placing the equipment for long-term storage, as well as in case of non-compliance of the storage conditions with the established requirements. In the above cases, the equipment receives multiple (multiple) defects, the elimination of which is carried out in stages: first defect detection, and then diagnosis.
 The article proposes for the first time an approach to the rational distribution of efforts between stages during the work of a team of specialists, which allows to minimize the average time to restore the working capacity of damaged communication equipment. At the same time, modern achievements in the field of technical diagnostics and metrology were used, the possibility of complex application of types of group search for defects, which was not taken into account in known works, was investigated. Functional dependencies were obtained and the procedure for determining individual stages was formalized to minimize the total time for restoring the operational capacity of damaged communication equipment. Examples of the use of the obtained results and evaluation of the implementation in the practice of military repair in field conditions are given. The proposed block diagram of the algorithm for calculating the distribution of defect time and diagnosis when eliminating multiple defects of military communication equipment by various methods. It is noted that the use of the proposed process of formalization of the rational distribution of the efforts of the team of hardware and technical support specialists in the restoration of military communication equipment with a weak degree of damage allows in each specific case, depending on the real conditions of the repair, to receive recommendations on achieving the minimum time to restore performance.
 It was established that the use of the obtained results allows to reduce the average time of restoration of damaged military communication equipment due to the rational distribution of the efforts of specialists between the stages of defect detection and diagnosis from 8% to 25%.

Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology

Abstract To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.

Review of Research on Fault Diagnosis of Rolling Bearings Based on Deep Learning

Deep learning has powerful capabilities in deep feature extraction and expression, and has been successfully applied in equipment fault diagnosis, overcoming the shortcomings of traditional diagnostic methods that rely on expert experience. It can save costs while improving diagnostic accuracy. This article briefly introduces three commonly used neural networks: Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM), and points out the problems in rolling bearing diagnosis and analyzes future development directions.

A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation

Substation equipment faults are typically related to the heating of equipment components. The hotline tap clamps of substation are critical components for carrying load currents and thermal fault potential. As a result, a new hybrid early warning approach for hotline tap clamp faults in substation equipment is presented. A two-dimensional Otsu algorithm is used to coarse-segment infrared images to minimize the subsequent complexity. Since the Chan–Vese (CV) model is insufficiently accurate for image segmentation with uneven grayscale, then the differential data obtained by the Prewitt operator to identify the goal edges are combined with the CV model to improve segmentation accuracy. The improved CV model achieves excellent segmentation of the hotline tap clamp in the substation. The temperature statistics are utilized for the segmented images, and the hotline tap clamp fault warning is realized based totally on the relative temperature difference. Finally, the experiments exhibit that the method can enhance the segmentation impact of infrared images and obtain the goal of fault warning.

A novel rolling bearing fault diagnosis method based on marginal spectrum

Fault diagnosis is an important method for maintaining the stable and safe running state of mechanical equipment. As most mechanical equipment faults are induced by the bearing assembly, bearing fault diagnosis is of considerable importance. At present, the mainstream intelligent diagnostic techniques include supervised learning and unsupervised learning. Supervised learning requires manual labeling and data classification, which is unfavorable for massive data amounts. Therefore, how to effectively use labeled data to increase the accuracy of diagnosis is critical, especially when the bearing failure cannot be labeled at the very beginning. This paper proposes a time–frequency analysis of the short-time Fourier transform and wavelet transform methods based on unsupervised learning. The time axis was integrated to obtain the marginal frequency of two frequency domains as a diagnostic feature, and then two clustering centroids were established automatically by the K-means of unsupervised learning. The signals were divided into two classes based on the nearest clustering centroid as the criteria for diagnosis. Finally, other bearings in different positions were classified and diagnosed using the nearest clustering centroid in the same experiment to verify the effectiveness of the method proposed in this study.