Fault Modes Research Articles

Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference

Bearing health is key for maintaining good performance and safety in rotating machinery. As the diagnosis of mechanical faults develops toward intelligence and automation, accurate and systematic fault diagnosis algorithms are imperative. Focusing on the diagnosis of rolling bearing failures, this study utilizes a sliding time window to extract essential data segments. A series of signal processing techniques, including filtering, amplitude–frequency analysis, Hilbert envelope analysis, and energy analysis, is applied to establish a comprehensive dataset. For extraction of the hidden properties of the data, the recurrence quantity spectrum is defined for the input of the neural network. The goal is to obtain a cleaner dataset with enhanced features. A convolution neural network is constructed. Different activation functions in the activation layer are compared for better fault diagnosis algorithms. The established feature matrices are specifically defined to accurately identify the subtlest defects of bearings, thereby facilitating early detection. The proposed procedure distinguishes various fault modes. As for the multidimensional complexities of fault signals, this study carries out a comprehensive comparison of energies, recurrence quantification, and amplitude–frequency characteristics of bearing fault detection to assess the accuracy, computational efficiency, and robustness of bearing fault diagnosis. The proposed method and bearing fault detection procedures have potential in practical applications.

A surface-shunting method for the prevention of a fault-mode-induced quench in high-field no-insulation REBCO magnets

In this paper, we apply a surface-shunting method to prevent quenches in no-insulation (NI) REBCO magnets triggered by external failures of magnet current leads or power suppliers (i.e., fault mode). In a high-field magnet system, an NI coil may still be at risk during the mentioned quench events even if the whole magnet is well-designed, non-defective, and properly operated. The mechanism of this fault-mode quench initiation and propagation still remains unclear, complicating the development of reliable quench protection. Here, we present this mechanism to demonstrate a corresponding practical quench-preventive approach named surface shunting, which utilizes a low-temperature solder attached to the top and bottom of pancake coils. We validate the effectiveness of this approach by comparing the electromagnetic, thermal, and mechanical behaviors in the fault mode with and without the shunt. We conclude that the surface shunt suppresses the fault-mode quench initiation and propagation by redirecting the original turn-to-turn current and induced overcurrent out of the NI winding. We anticipate this work can provide a solution to improve the operational safety of high-field HTS NI magnets against quench and potential damage during fault modes.

Defect modulation and in-situ exsolution in Y2Ru2O7@NiFeP/Ru heterostructure for enhanced oxygen evolution reaction

Defect modulation and in-situ exsolution in Y2Ru2O7@NiFeP/Ru heterostructure for enhanced oxygen evolution reaction

Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes

As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.

Cross-domain transfer fault diagnosis by class-imbalanced deep subdomain adaptive network

In variable working conditions and class imbalances, fault diagnosis in gearbox systems becomes difficult due to the difficulty in learning the classification boundaries of the minority class. Moreover, the diversity in fault mode distributions under various working conditions poses significant challenges for domain adaptation. To this end, this work proposed a Class-imbalanced Deep Subdomain Adaptive Network (CIDSAN) for complex cross-domain fault diagnosis scenarios involving feature and label shifts. We design a convolutional neural network embedded with SimAM (SimAM-CNN) to extract global feature information from time-domain vibration signals. Subsequently, a novel cost-sensitive classifier is presented for class re-weighting, along with a Joint Supervised Classifier that refines intra-class and inter-class distances by re-weighting class weights in the latent space using cost-sensitive classifiers while leveraging LDAM regularization to adjust classification boundaries. Diagnostic case studies on two datasets under various working conditions validate the effectiveness and superiority of the proposed approach. The code https://github.com/Zjy0121/CIDSAN.

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network.

Coupling faults that simultaneously occur during the operation of mechanical equipment are widespread. These faults encompass a diverse range of high-order coupling relationships, involving multiple base fault types. Based on the advantages of hypergraphs for higher-order relationship descriptions, two coupling fault diagnosis architectures based on the hypergraph neural network are proposed in this paper: 1. In the coupling fault diagnosis framework based on feature generation, the base faults serve as the hypergraph nodes, and each hyperedge connects the base faults. The generator, which consists of the hypergraph neural network, generates coupling faults as negative samples to enforce regularization constraints for the discriminator training. 2. In the coupling fault diagnosis framework based on feature extraction, each node represents a fault mode, and each hyperedge connects nodes with common failure modes. The multi-head attention mechanism extracts the features of base faults, and the common fault features in a hyperedge are aggregated via the hypergraph neural network. The inner product correlation is used to diagnose the fault modes. The results show that the diagnostic accuracy for coupling faults with the two frameworks reaches 88.6% and 86.76%, respectively. Both frameworks can be used for the diagnosis and analysis of high-order coupling faults.

Fault isolation considering real mechanical coupling effects with application to liquid oxygen/kerosene staged combustion rocket engines

Liquid rocket engines are crucial for space exploration, necessitating reliable fault diagnosis systems. This paper focuses on fault isolation in liquid oxygen (LOX)/kerosene staged combustion rocket engines. Addressing deficiencies in current methods, we first propose a theorem to evaluate the adaptability between the analytical model and fault factors based on available sensors. Through the analysis of fault mechanisms, this paper defines fault modes using an innovative multi-factor coupling approach based on engine operational principles. Moreover, a fault isolation method with two steps is established, incorporating the optimal selection of sensors to accommodate robust requirements. This method features a clear process, low computational complexity, and comprehensive fault coverage, and it can also be applied to systems with similar complex operational mechanisms.

An adaptive imbalance robust graph embedding broad learning system fault diagnosis for imbalanced batch processes data

Modern batch processes develop toward higher levels, and devices usually work at normal conditions with generally very few faults. Therefore, fewer fault data are collected than normal data, which makes the normal and fault modes imbalanced, and the fault diagnosis model is incapable focusing on the minority of fault samples as much as on most normal samples, consequently leading to insufficient generalization ability for the model. For that, we propose an adaptive imbalance-robust graph embedding broad learning system (AI-RGEBLS) in this paper. Firstly, it achieves adaptive correction of unbalanced samples by niche technique and synthetic minority over-sampling technique (SMOTE) with improved Mahalanobis distance. Then, broad learning system (BLS) is regularized by graph embedding, and further introduced the L2,1 norm constraint, which makes it possible to enhance the robustness of the model while considering the local manifold information of the data in the feature extraction process. Finally, the incremental learning approach is applied to the model to avoid the disastrous forgetting problem caused by training the whole model from zero. The effectiveness of the proposed method is verified by penicillin fermentation process and semiconductor etching process. It can effectively improve the speed of model training and provide better fault diagnosis for imbalanced batch processes compared with the existing methods.

Influence of surface defect modulation based on ionic liquids on performances of perovskite films

Influence of surface defect modulation based on ionic liquids on performances of perovskite films

Ultrahigh Power Factor of Sputtered Nanocrystalline N-Type Bi2Te3 Thin Film via Vacancy Defect Modulation and Ti Additives.

Magnetron-sputtered thermoelectric thin films have the potential for reproducibility and scalability. However, lattice mismatch during sputtering can lead to increased defects in the epitaxial layer, which poses a significant challenge to improving their thermoelectric performance. In this work, nanocrystalline n-type Bi2Te3 thin films with an average grain size of ≈110nm are prepared using high-temperature sputtering and post-annealing. Herein, it is demonstrated that high-temperature treatment exacerbates Te evaporation, creating Te vacancies and electron-like effects. Annealing improves crystallinity, increases grain size, and reduces defects, which significantly increases carrier mobility. Furthermore, the pre-deposited Ti additives are ionized at high temperatures and partially diffused into Bi2Te3, resulting in a Ti doping effect that increases the carrier concentration. Overall, the 1µm thick n-type Bi2Te3 thin film exhibits a room temperature resistivity as low as 3.56 × 10-6 Ω∙m. Notably, a 5µm thick Bi2Te3 thin film achieves a record power factor of 6.66mWmK-2 at room temperature, which is the highest value reported to date for n-type Bi2Te3 thin films using magnetron sputtering. This work demonstrates the potential for large-scale of high-quality Bi2Te3-based thin films and devices for room-temperature TE applications.

Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots.

An effective CNN-MHSA method for the fault diagnosis of ZPW-2000A track circuit

The mainstream methods for fault diagnosis of ZPW-2000A track circuits are overly complex in extracting features from sequence data, and their feature extraction capability is relatively limited, thus impacting the performance of fault diagnosis. To address this issue, we propose a fault diagnosis model for track circuits, named as convolutional neural network incorporating multi-head self-attention mechanism (CNN-MHSA). On one hand, the local features of sequence data are locally sensed and extracted by the convolutional layer; on the other hand, the global features of sequence data are captured by establishing long-distance dependency relationships through the multi-head self-attention layer. The dual extraction of local and global features enables accurate diagnosis of faults. Finally, we collect 27 fault modes and 2 normal operation modes in the simulation system and conduct a large number of numerical experiments. The experimental results show that the fault diagnosis accuracy of the CNN-MHSA model in this paper can reach 97.45%, which is an improvement of 0.64%, 0.44%, 0.44%, 0.44%, 0.33%, 0.22%, and 0.11% compared with models such as Decision Tree, Random Forest, CatBoost, long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM. It also has obvious advantages in evaluation metrics such as precision, recall, Macro-F1, and Micro-F1, as well as other evaluation metrics. Therefore, the model significantly improves the comprehensive performance of ZPW-2000A track circuit fault diagnosis and can be used as a more effective fault diagnosis method.

Prognostics and Health Management for Electrified Aircraft Propulsion: State of the Art and Challenges

Abstract In recent years, the aviation industry has witnessed a transformative wave of innovation in electrified aircraft propulsion (EAP), driven by sustainability and efficiency goals. Integration of novel electrical subsystems, including high-voltage power electronics, motors/generators, and energy storage devices, has introduced intricate complexities. In this context, an intensified focus on Prognostics and Health Management (PHM) is imperative, considering the heightened reliability needs in a transportation propulsion application. This paper analyzes the current state-of-the-art in PHM applicable to various EAP systems and components crucial for the functioning of electric aircraft. Typical fault modes and fault management strategies are analyzed at various levels of systems hierarchy. An integral aspect of our investigation involves the identification of critical gaps within existing PHM frameworks, guiding the research agenda for enhanced reliability and performance. Moreover, the distributed nature and complexity of EAP systems underscore the importance of Model-Based Systems Engineering (MBSE). We advocate for the exploration of MBSE not only to inform the design of PHM solutions but also to facilitate certification and Verification and Validation activities. Additionally, the paper offers insights into existing tools and simulation software capable of integrating traditional gas turbine modules with electric subsystems, as well as simulating various faulty conditions in EAP relevant to PHM development. Key gaps in these tools are emphasized, drawing attention to areas that require further refinement and development. This comprehensive exploration aims to pave the way for future advancements in PHM tailored for the unique challenges posed by EAP systems.

Intelligent Fault Diagnosis Method for Constant Pressure Variable Pump Based on Mel-MobileViT Lightweight Network

The sound signals of hydraulic pumps contain abundant key information reflecting their internal mechanical states. In environments characterized by high temperatures or high-speed rotation, or where sensor deployment is challenging, acoustic sensors offer non-contact and flexible arrangement features. Therefore, this study aims to develop an intelligent fault diagnosis method for hydraulic pumps based on acoustic signals. Initially, the Adaptive Chirp Mode Decomposition (ACMD) method is employed to remove environmental noise from the acoustic signals, enhancing the feature signals. Subsequently, the Mel spectrum is extracted as the acoustic fingerprint features of various fault states of the hydraulic pump, and these features are used to train the MobileViT network, achieving accurate identification of the different fault modes. The results indicate that the proposed Mel-MobileViT model effectively identifies and classifies various faults in constant pressure variable pumps, outperforming other models. This study not only provides an efficient and reliable intelligent method for the fault diagnosis of critical industrial equipment such as hydraulic pumps, but also offers new perspectives on the application of deep learning in acoustic pattern analysis.

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models.

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Enhancing efficiency of large cold store refrigeration systems through automated fault identification and intelligent energy optimization

Refrigeration systems in large cold stores frequently operate suboptimally due to component faults, leading to significant energy wastage and high carbon emissions. This study introduces a novel procedure that leverages data mining to automatically analyze and identify faults, thereby enhancing the intelligence of refrigeration equipment. The research focused on abnormal suction temperatures of compressors during the defrosting of air coolers in a large cold store. Through theoretical analysis and key data acquisition, the root cause of defrosting issues was traced to the abnormal operation of gas-powered suction stop valves, causing leakage of high-pressure hot gas. Clustering methods, Self-Organizing Maps (SOM), were utilized to classify system states and achieved high accuracy rates of 88.6 % to 93.8 % for the three fault modes during the defrosting process, respectively. The resolution of defrosting faults resulted in an energy consumption reduction of up to 18.3 %, aligning with global sustainability initiatives. The study also evaluated the carbon emission reduction, providing a comprehensive approach to improving the efficiency and environmental impact of cold store operations.

Defect-rich carbon nanosheets derived from p(C3O2)x for electromagnetic wave absorption applications

Defect modulation strategies have been shown to be an effective way to design efficient EMW absorbing materials, but the coexistence of multiple loss mechanisms due to the complexity of the existing models makes it difficult to elucidate the mechanism by which defect-induced dielectric losses dominate. In this work, p(C3O2)x is applied for the first time in the field of EMW absorption and the concentration of defects in the sample is controlled by changing the pyrolysis temperature. In addition, the unique molecular structure of p(C3O2)x enables the prepared samples to completely eliminate the interference of interfacial polarization and magnetic loss on EMW dissipation. The results show that the dielectric loss induced by defects significantly enhances the EMW absorption performance as the concentration of defects increases, but excessive defects lead to a sudden drop in the conductivity of the sample and reduce the EMW absorption performance. In which, the RLmin of OC-800 can reach −51.0 dB, and the EAB of OC-900 can go up to 5.6 GHz at only 1.6 mm. Finally, CST simulation verified the potential application of the prepared absorber in real scenarios. This work has improved the theoretical basis of the effect of defect-induced dielectric loss on EMW absorbing properties, and the simple synthetic raw materials and routes have made the industrialized production of highly efficient EMW absorbing materials possible.

The Link between Endogenous Pain Modulation Changes and Clinical Improvement in Fibromyalgia Syndrome: A Meta-Regression Analysis.

Conditioned pain modulation (CPM) and temporal summation (TS) tests can measure the ability to inhibit pain in fibromyalgia syndrome (FMS) patients and its level of pain sensitization, respectively. However, their clinical validity is still unclear. We studied the association between changes in the CPM and TS tests and the clinical improvement of FMS patients who received therapeutic intervention. We systematically searched for FMS randomized clinical trials with data on therapeutic interventions comparing clinical improvement (pain intensity and symptom severity reduction), CPM, and TS changes relative to control interventions. To study the relationship between TS/CPM and clinical measures, we performed a meta-regression analysis to calculate odds ratios. We included nine studies (484 participants). We found no significant changes in TS or CPM by studying all the interventions together. Our findings show that this lack of difference is likely because pharmacological and non-pharmacological interventions resulted in contrary effects. Non-pharmacological interventions, such as non-invasive neuromodulation, showed the largest effects normalizing CPM/TS. Meta-regression was significantly associated with pain reduction and symptom severity improvement with normalization of TS and CPM. We demonstrate an association between clinical improvement and TS/CPM normalization in FMS patients. Thus, the TS and CPM tests could be surrogate biomarkers in FMS management. Recovering defective endogenous pain modulation mechanisms by targeted non-pharmacological interventions may help establish long-term clinical recovery in FMS patients.

Operational reliability assessment of complex mechanical systems with multiple failure modes: An adaptive decomposition-synchronous-coordination approach

To effectively perform Complex Mechanical Systems Operational Reliability Assessment (CSORA) with multiple failures, the Adaptive Decomposition-Synchronous-Coordination Approach (ADSCA) is proposed by integrating Decomposition-Synchronous-Coordination strategy, adaptive modeling technology, surrogate model and multi-objective reliability assessment theory. In which, the main problem is decomposed into related sub-problems using a Decomposition-Synchronization-Coordination strategy, with sub-models coordinated to achieve synchronous mapping between multiple variables, thereby enhancing efficiency in large multi-subsystem problems. The adaptive modeling technique is adopted to construct a series of interrelated sub models based on the surrogate model as the basis function, improving performance. A multi-objective reliability assessment theory is adopted to achieve CSORA under multiple fault modes, where multiple weak links are integrated to comprehensively evaluate the reliability of complex systems. The significance of the ADSCA lies in its ability to manage complexity, enhance scalability, and improve the accuracy and efficiency of reliability assessments in complex mechanical systems. To demonstrate the effectiveness of the proposed approach, two illustrative examples are used. This includes approximation and probability analysis of nonlinear functions that exhibit multiple responses, as well as reliability analysis of temperature in civil aircraft braking system. The study can provide theoretical reference for the reliability evaluation of multi-failure operation in complex mechanical systems.