Fault Diagnosis Technology Research Articles

Deep Learning in Security of Internet of Things

Internet-of-Things (IoT) technology is increasingly prominent in the current stage of social development. All walks of life have begun to implement the IoT integration technology, so as to strive to promote industrial modernization, intelligence, and digitalization. In this case, how to link high-risk network activities with entities has become the primary issue for promoting industrial development. However, at this stage, the security issues in the development of the IoT technology have contradictions that are difficult to resolve. According to this situation, how to make system defense intelligent and replace manual monitoring has become the future of the development of security architecture. This article combines existing security research to explore the possibility of deep learning (DL) in upgrading the IoT security architecture, discusses how the IoT can identify and respond to cyber attacks, and how to encrypt edge data transmission. Moreover, this article discusses security research in application fields, such as Industrial IoT, Internet of Vehicles, smart grid, smart home, and smart medical. Then, we summarized the areas that can be improved in future technological development, including sharing computing power through the edge network processing unit (NPU) central device and closely combining the environmental simulation model with the actual environment, as well as malicious code detection, intrusion detection, production safety, vulnerability detection, fault diagnosis, and blockchain technology.

Revising the application of cross-spectrum processing in motion parameter estimation for harmonic sources

Single-receiver motion parameter estimation is an effective and economical technology for passive source localization and train-bearing fault diagnosis, in which time-consuming time-frequency analysis (TFA) methods are widely used to suppress noise when extracting the continuous Doppler shift of the overhead pass. Cross-spectrum processing is a potential way to improve the computational efficiency of TFA methods, but its application is overshadowed by the phenomena of unknown Doppler shift offset and power spectrum estimation error. In this paper, conventional cross-spectrum processing is proven to be an approximation trick for power spectrum estimation in a small frequency interval, and the two phenomena are fully explained by the frequency aliasing of bandpass sampling and the approximation error. On this basis, an revised framework for applying the cross-spectrum processing is provided. Processing results of the SWellEx-96 experiment data demonstrate that the computational efficiencies of spectrogram and a parameterized TFA method could be improved up to 85% and 88.2%, respectively, without a noticeable impact on the accuracy of parameter estimates.

Fault detection and diagnosis for liquid rocket engines with sample imbalance based on Wasserstein generative adversarial nets and multilayer perceptron

The reliability of liquid rocket engines (LREs), which are the main propulsion device of launch vehicles, cannot be overemphasised. The development of fault detection and diagnosis (FDD) technology for LREs can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. With the rapid development of artificial intelligence technologies such as machine learning and artificial neural network, data-driven FDD methods have gained increasing attention. However, the scarcity of engine fault samples limits the application of this methods. We proposed a method combining Wasserstein generative adversarial nets (WGANs) and multilayer perceptron (MLP) to perform FDD for LREs with sample imbalance. Wasserstein generative adversarial nets were trained using the fault data from the actual hot-firing ground test of a large LRE. Considerable fault data were generated to expand the data set to balance the ratio of positive and negative samples. Subsequently, the expanded data set was used to train the MLP for FDD of a large LRE. The results showed that the samples generated by the WGAN were authentic, confirming the application of the proposed method as a novel and effective tool for establishing a complete LRE fault database. Furthermore, the diagnosis times of the proposed method on five fault tests were advanced by 0.66, 15.82, 0.24, 0.14 and 1.08 s in relation to those of the conventional red-line cut-off system. Compared with support vector machine and adaptive threshold algorithm, the proposed method also performed better.

Research on robotic manipulator fault detection and diagnosis technology based on machine vision in complex environments

AbstractCurrently, machine vision‐based fault detection and diagnosis technology for robotic manipulators is widely used. However, traditional machine vision has difficulty identifying manipulator failure in complex environments with dim lighting and large texture differences and the influence of such factors as image motion blur caused by robotic manipulator movement. This article discusses the failure factors of mechanical manipulators and systematically analyzes various links leading to failure and the current technology limitations. First, a gradient‐based semantic segmentation method is proposed to extract targets quickly and accurately for the grasped object and complex surrounding environment. Second, when the vision and grasped object have relative movement in dim environment, a multiframe image registration and fusion method are proposed to obtain high‐quality, clear image data. Then, a machine‐based method is adopted to learn the fault detection and diagnosis methods that fuse internal and external sensors. Finally, a physical system is built to verify the three aspects of the target extraction effect: image clarity, fault detection speed, and diagnosis accuracy, reflecting the superiority of this algorithm.

Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods.

Gas path fault diagnosis for gas turbine engines with fully operating regions using mode identification and model matching

Gas path fault diagnosis is key to improving the reliability and safety of gas turbine engines. Flexible operating conditions bring obstacles to performing accurate gas path performance analysis. Most of the existing methods are developed for specific operating conditions, which are difficult to adapt to fully operating regions. The operating mode identification and targeted diagnostic model matching are effective technologies to solve the gas path fault diagnosis under fully operating regions, which improves diagnostic accuracy and efficiency. The fully operating regions are classified into four typical operating modes, and the targeted diagnostic models are matched according to the mode features. For the typical start-stop state and high dynamic state, the small deviation diagnostic model and transient diagnostic model are established and verified by real fault cases. The small deviation diagnostic model based on boundary parameters reduces the influences of operating conditions on diagnostic results, it accurately monitors the health states. The transient diagnostic model driven by the dynamic model and a designed hybrid solution algorithm markedly improves diagnostic accuracy and efficiency. It shows better performance for the mixed gas path fault modes, more stable diagnostic results, and higher diagnostic efficiency. The proposed technical framework provides an effective way for the fault diagnosis of gas turbine engines under fully operating regions.

Sensing with sound enhanced acoustic metamaterials for fault diagnosis

Cost-effective technology for condition monitoring and fault diagnosis is of practical importance for equipment maintenance and accident prevention. Among many fault diagnosis methods, sound-based sensing technology has been highly regarded due to its rich information, non-contact and flexible installation advantages. However, noise from the environment and other machines can interfere with sound signals, decreasing the effectiveness of acoustic sensors. In this paper, a novel trumpet-shaped acoustic metamaterial (TSAM) with a high enhancement of sound wave selection is proposed to detect rotating machinery faults. Firstly, a numerical calculation was carried out to test the characteristics of the proposed metamaterials model. Secondly, a finite element simulation was implemented on the model to verify the properties of the designed metamaterials. Finally, an experiment was conducted based on an electrical fan to prove the effectiveness of the designed metamaterials. The results of the signal-to-noise ratio show more than 25% improvement, consistently demonstrating the potentiality of the designed acoustic metamaterials for enhancing the weak fault signal in acoustic sensing and the capabilities of contributing to a more cost-effective fault diagnosis technology.

Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage

In recent years, wind turbines have shown a maximization trend. However, most of the wind turbine blades operate in areas with a relatively poor natural environment. The stability, safety, and reliability of blade operation are facing many challenges. Therefore, it is of great significance to monitor the structural health of wind turbine blades to avoid the failure of wind turbine outages and reduce maintenance costs. This paper reviews the commonly observed types of damage and damage detection methods of wind turbine blades. First of all, a comprehensive summary of the common embryonic damage, leading edge erosion, micro-cracking, fiber defects, and coating defects damage. Secondly, three fault diagnosis methods of wind turbine blades, including nondestructive testing (NDT), supervisory control and data acquisition (SCADA), and vibration signal-based fault diagnosis, are introduced. The working principles, advantages, disadvantages, and development status of nondestructive testing methods are analyzed and summarized. Finally, the future development trend of wind turbine blade detection and diagnosis technology is discussed. This paper can guide the use of technical means in the actual detection of wind turbine blades. In addition, the research prospect of fault diagnosis technology can be understood.

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

Inspection Technology of Power Communication Network Based on Machine Vision Graphic Recognition

With the continuous improvement of maintenance management level and the continuous progress of fault diagnosis technology, equipment condition maintenance has also gradually entered the stage of market use. Especially, in the electric power industry, with the development of the research and production practice of condition maintenance theory, its application areas are becoming more and more extensive. In recent years, with the increasing popularity of computer network technology, the development of communication network monitoring technology has also made great progress. However, the monitoring of communication equipment in China is still in the primary stage, and the complexity of the equipment and the diversity of the equipment make the research on its condition detection a very challenging task. The study introduces the application of computer vision-based graphic recognition technology in power communication networks, which includes two modules: FasterR-CNN and RPN. The model provides real-time monitoring of various performance indicators of power communication network equipment and feedback on its working status, repairs the equipment according to the monitoring results, timely detects potential safety hazards, and makes a maintenance cycle reasonable planning, ensuring the normal operation of the communication network.

A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis.

The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning (BayesianPDL) framework is proposed and then achieves fault classification. A parallel deep learning (PDL) framework is proposed to solve the problem of difficult feature extraction of bearing faults. Next, the weights and biases in the PDL framework are converted from deterministic values to probability distributions. In this way, an uncertainty-aware method is explored to achieve reliable machine fault diagnosis. Taking the fault signal of the gearbox output shaft bearing of a wind turbine generator in a wind farm as an example, the diagnostic accuracy of the proposed method can reach 99.14%, and the confidence in diagnostic results is higher than other comparison methods. Experimental results show that the BayesianPDL framework has unique advantages in the fault diagnosis of wind turbine bearings.

A Novel Method for Enhanced Demodulation of Bearing Fault Signals Based on Acoustic Metamaterials

Rolling bearings fault diagnosis technology plays a significant role in modern industries. In this article, a novel method to diagnose bearing faults via acoustic metamaterials is proposed and experimentally validated by selecting and enhancing the optimal resonance frequency band for demodulation. The proposed method is performed by analysing acoustic signals, which is convenient due to its noncontact measurement. Different from conventional denoising techniques, the proposed method is implemented by enhancing the resonance frequency band via acoustic metamaterials instead of constructing filters to wipe out noise. Consequently, all the useful information of fault signals are remained and the detection limits of current acoustic sensing systems are improved because of the enhanced fault signals. Additionally, the optimal resonance band is sought by changing measured positions based on the frequency-selective enhancement property of the designed metamaterial. By comparing with spectral kurtosis-based method, the proposed method is more superior to extract bearing fault features at low signal to noise ratios. All the presented results indicate that the proposed method is effective to extract bearing fault features.

Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

Due to complicated transfer paths and strong background noise interference, the fault pattern information deeply hides in common features of the vibration signal at the engine surface. In this study, the refined composite multiscale fuzzy entropy (RCMFE) used to measure the irregularity and self-similarity of time series is proposed to quantify the feature of various fault patterns. Followed by RCMFE, the features dug out are recognized by a parameter-adaptive support vector machine based on the firefly algorithm (FASVM). After putting forward the diagnosing schematics, the RCMFE-FASVM is applied to a fault diagnosis case of a diesel engine on a test rig. A comparative analysis of the four methods to extract features and the four methods to recognize fault patterns are conducted. Results indicate the proposed method has superior recognition performance and can effectively identify the working states of the diesel engine, contrasting the existing methods. Under the small samples and features task of identifying the working states of a diesel engine, the recognition rate of the proposed method with more stability can reach 98.2%, which is larger than other methods. Given the superior performance of the proposed method, the number of input features and training samples should vary from 8 to 20 and from 35 to 50.

Review on online operation insulator identification and fault diagnosis based on UAV patrol images and deep learning algorithms

Artificial intelligence has great potential for use in smart grids. Power system image recognition based on artificial intelligence is an important research direction. The insulator is essential equipment for the power grid and is related to operational safety. Online operating insulator location identification and fault diagnosis technologies based on unmanned aerial vehicle (UAV) patrol the images, and deep learning algorithms have been continuously suggested and developed. These technologies have achieved good results in practical application. By compiling the recent literature on insulator detection technology, three common application scenarios and research difficulties are uncovered: The need for increased detection accuracy and real-time speed; faulty image recognition of complex backgrounds and target occlusion; and multiscale object and small object detection improvements. At the same time, the improved algorithms in the literature are comprehensively summarized, and the performance evaluation indices of various algorithms are compared.

Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis

In order to solve the problem that a large number of vibration signals cannot be transmitted in real time in the application of wireless sensor networks (WSNs) in mechanical fault diagnosis, a mechanical fault diagnosis method based on multilevel and hierarchical information fusion of WSNs was proposed. In this method, the cluster tree network structure is used to expand the coverage of network monitoring, and WSNs information fusion is divided into three levels: data-level fusion, feature-level fusion, and decision-level fusion. The terminal node performs data-level fusion on the original vibration information to extract feature information; the cluster-head node performs feature-level fusion on the feature information to obtain pattern recognition results; and the gateway node performs decision-level fusion on the recognition results to evaluate the running status of mechanical equipment. The results show that the slight damage fault of the bearing inner ring can be accurately diagnosed by decision-level fusion based on four groups of probability distribution functions. According to the statistics of 30 test results, the fault recognition rate is 83.3%. The method can be applied to mechanical fault diagnosis effectively.

Defect Size Evaluation of Cylindrical Roller Bearings with Compound Faults on the Inner and Outer Races

Faults in cylindrical roller bearings are one of the main contributors to major faults in rotating machinery. The development of bearing fault diagnosis technologies is key to measuring the performance, status, and risk of failure of rolling-element bearings and has attracted extensive attention from industry and academia. When faults arise, they are often not a single fault but a compound fault, such as the simultaneous failure of the inner and outer races. In this paper, a method for evaluating the size of compound faults on the inner and outer races of cylindrical roller bearings is proposed. The dynamic modeling method developed by Gupta is employed to create a dynamic model for compound faults on the inner and outer rings of rolling bearings that allows the time domain signal of the vibration responses of compound faults on the inner and outer races to be obtained. Adopting an improved continuous harmonic wavelet packet decomposition method for the decomposition and reconstruction of the compound fault signal, we arrive at the corresponding single-point fault signal. The relationship between defect size and key metrics of the vibration, such as root mean square acceleration (RMS), peak, crest factor (CF), kurtosis, and level crossing rate (LCR), is investigated. The results show that there is a strong linear correlation between LCR and defect size, which can be used to evaluate the size of the defect. Experimental data for cylindrical roller bearings with compound faults on the inner and outer races are examined to verify the results.

Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM.

Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of engines, a recognition method based on the combination of multi-scale convolutional neural networks (MsCNNs) and bidirectional long short-term memory neural networks (BiLSTM) is proposed. Firstly, the MsCNN is used to extract the multi-scale features from the flight data. Subsequently, the spatial and channel weights are corrected using the weight adaptive correction module. Then, the BiLSTM is used to extract the temporal dependencies in the data. The Focal Loss is used as the loss function to improve the recognition ability of the model for confusable samples. L2 regularization and DropOut strategies are employed to prevent overfitting. Finally, the established model is used to identify the working conditions of an engine sortie, and the recognition results of different models are compared. The overall recognition accuracy of the proposed model reaches over 97%, and the recognition accuracy of transition conditions reaches 94%. The results show that the method based on MsCNN–BiLSTM can effectively identify the aeroengine working conditions including transition conditions accurately.

Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals.

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters.

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.

Double Redundancy Electro-Hydrostatic Actuator Fault Diagnosis Method Based on Progressive Fault Diagnosis Method

The electro-hydrostatic actuator (EHA) is the key component of most electric aircraft, and research on its fault diagnosis technology is of great significance to improve the safety and reliability of aircraft flight. However, traditional fault diagnosis methods only focus on partial failures and cannot completely diagnose the whole EHA system. In this paper, the progressive fault diagnosis method (PFDM) is proposed for overall diagnosis of whole EHA system, which can be divided into four levels for health detection and fault diagnosis of the overall EHA system. PFDM combines fault diagnosis methods based on Kalman filter, threshold, logic, and EHA system analysis model to diagnose the whole EHA system layer by layer. At the same time, in order to ensure the normal operation of the EHA system after fault diagnosis, double redundancy design is creatively carried out for the EHA system to facilitate system reconstruction after fault detection. It can be continuously modified according to different EHA system parameters and measured signals to improve the accuracy of fault diagnosis. The experimental results show that PFDM can accurately locate and identify 22 faults of the double redundancy EHA system by using the accurate EHA system mathematical model. PFDM improves the fault diagnosis response time to 4 ms, greatly improving the safety and reliability of the double redundancy EHA system.