Fault Monitoring Research Articles

Cloud Server-Assisted Remote Monitoring and Core Device Fault Identification for Dynamically Tuned Passive Power Filters

Reliability and safety are crucial for the operation of a dynamically tuned passive power filter (DTPPF). Safe performance of DTTPFs implies complete normal filtering without failure within a specified period. To prevent potential disaster or economic loss, it is desirable to achieve early warning of any core device faults in a DTPPF based on its running state and to optimize its harmonic mitigation performance. In this paper, we explore effective methods for identifying core device faults in DTPPFs. First, we summarize the characteristic parameters of faults, running state parameters, parameters required for fault monitoring, and fault type parameters. Then, a cloud server-assisted remote monitoring and fault identification system for DTPPF is proposed, which consists of monitoring system’s architecture and cloud servers’ software architecture as well as software design of the back-end service layer and functional design of the front-end application layer. Our experiments demonstrate that the proposed system can monitor the real-time operational status of the DTPPF, enabling remote diagnosis and identification of core device faults. Moreover, it is user-friendly, as it is capable of optimizing equipment maintenance schedules and utilizing manufacturers’ service capacities. Therefore, this research provides a theoretical foundation for harmonic mitigation in low-voltage distribution networks and is valuable for practical engineering applications in industrial power grids.

Similarity index of the STFT-based health diagnosis of variable speed rotating machines

Fault diagnosis and health monitoring of industrial rotating machines are of paramount importance for ensuring the reliability, safety, and efficiency of modern industrial operations. This paper proposes a Short-Time Fourier Transform (STFT)-based fault diagnosis approach for industrial rotating machinery. In this proposed model, the STFT of the reference vibration signals is evaluated and compared with the STFT of the other testing vibration signals to diagnose the fault types. Three different similarity operators: Euclidean distance, cosine similarity, and structural similarity are used to conclude the similarity index between the reference signal and test signal. By using variable speed vibration data with different fault types, the proposed model can better simulate real-world conditions and improve the accuracy and effectiveness of fault diagnosis. The results from the confusion matrices, heat maps, and t-SNE plots demonstrate the effectiveness of the proposed method for fault diagnosis and monitoring of variable-speed rotating machines using vibration signals. It is concluded that the structural similarity index proved to be a promising approach for accurate fault diagnosis in variable-speed rotating machines. The results are also compared with the existing approaches in the literature and it was concluded that the proposed model attains the highest accuracy for the variable speed rotating machines.

A Survey on Fault Identification System in OTN using Neural Network

The diversity of information and services that are sent via optical networks is growing day by day, hence the survivability of optical networks has emerged as a significant research issue in recent years. The location of the fault in an optical transmission network (OTN) is very important when investigating the survivability of optical networks. The first step is to create network model with three channel, after which it is necessary to analyze common alarm data, as well as fault monitoring points and common fault sites. The fault location field of OTN incorporates the artificial neural network (ANN), which is used to identify whether or not a probable fault site is present. In this paper, we had done a review of earlier research work in this field and identified the research gap in the introduction of neural networks into the area of fault identification in optical transport networks.

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities.

High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system's health and enabling proactive maintenance and decision making.

FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model’s performances are analyzed in terms of local and global models.

A Machine Learning–Based Approach for Fault Detection in Power Systems

Machine learning techniques are becoming popular for monitoring the health and faults of different components in power systems, including transformers, generators, and induction motors. Normally, fault monitoring is performed based on predetermined healthy and faulty data from the corresponding system. The main objective of this study was to recognize the start of a system fault using a Support Vector Machine (SVM) approach. This technique was applied to detect power system instability before entering an unstable condition. Bus voltages, generator angles, and corresponding times before and after faults were used as training data for the SVM to detect abnormal conditions in a system. Therefore, a trained SVM would be able to determine the fault status after providing similar test data once a disturbance has been resolved.

Autonomous fault detection and diagnosis for permanent magnet synchronous motors using combined variational mode decomposition, the Hilbert-Huang transform, and a convolutional neural network

The continuous and online monitoring of the condition of electrical machines is key to their safe operation. This study introduces a novel fault detection and diagnosis technique for continuous monitoring of faults in permanent magnet synchronous motors (PMSM). The proposed method relies solely on built-in sensors (stator phase currents only) to detect three types of faults: inter-turn short circuit, partial demagnetisation, and static eccentricity. Our fault detection and diagnosis strategy was developed by combining variational mode decomposition (VMD), the Hilbert-Huang transform (HHT) and a convolutional neural network (CNN). The VMD is first applied to the stator phase current signals to analyse the characteristic behaviour of the current signals by decomposing the current signals into several intrinsic mode functions. The intrinsic mode functions of the healthy and faulty signals are compared, and that with the frequency shift characteristics is selected. HHT is then applied to extract the fault feature by calculating the instantaneous frequency. Finally, the instantaneous frequency feature is fed into the CNN, which is designed to detect and classify motor faults. Experimental results clearly show that the variation of the instantaneous frequency of the PMSM, working at different operating states, can be utilised for condition monitoring and fault detection.

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions.

Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.

A Prognostic Approach to the Resonance Failure Problem by Triple Sensing Technology

Within this study, resonance phenomenon, which is one of the crucial problems in mechanical constructions, has been analyzed with respect to oil starvation failure in a ball bearing. A unique test rig is designed, constructed, and placed in a laboratory ambience. A ball bearing on the electrical motor, which is a component of the test rig, has been selected for acquisition of data within triple sensing technology in vibration, acoustic, and electrical consumption through testing conditions. The target of that study is condition monitoring of oil starvation fault and resonance fault for comparison of various predictive maintenance methods. The testing was carried out within the electrical frequency of 40.5 Hz, which actuated the electrical motor in order to identify the rotation speed. According to the analyzed results, oil starvation fault and resonance fault is most accurately inspected by vibration analysis.

Research on Drone Fault Detection Based on Failure Mode Databases

Drones are widely used in a number of key fields and are having a profound impact on all walks of life. Working out how to improve drone safety through fault detection is key to ensuring the smooth execution of tasks. At present, most research focuses on fault detection at the component level as it is not possible to locate faults quickly from the global system state of a UAV. Moreover, most methods are offline detection methods, which cannot achieve real-time monitoring of UAV faults. To remedy this, this paper proposes a fault detection method based on a fault mode database and runtime verification. Firstly, a large body of historical fault information is analyzed to generate a summary of fault modes, including fault modes at the system level. The key safety properties of UAVs during operation are further studied in terms of system-level fault modes. Next, a monitor generation algorithm and code instrumentation framework are designed to monitor whether a certain safety attribute is violated during the operation of a UAV in real time. The experimental results show that the fault detection method proposed in this paper can detect abnormal situations in a timely and accurate manner.

Sideband analysis of cracked planetary gear train considering output shaft radial assembly error

In a planetary gear train (PGT), the accuracy of gear tooth crack fault monitoring is affected by many external factors, such as the radial assembly error of the output shaft. In this paper, a three-dimensional loaded tooth contact analysis (3DLTCA) model is proposed considering the variation of the center distances and tooth root crack fault of the planet gear. The time-varying meshing stiffness (TVMS) caused by the crack fault of the planet gear and the radial assembly error of the output shaft has been revealed. By introducing the TVMS as the vibration excitation source into a coupling PGT-shaft dynamic model, the dynamic response under the joint influence of assembly error and the crack fault is calculated. By comparing with the experimental results, the distribution law of sidebands caused by the radial assembly error of the output shaft and the crack fault of the planet gear is obtained. Based on the distribution law of sidebands, a signal decomposition method is proposed. Sidebands caused by the assembly error and the crack fault are separated. Through the validation of the condition indicators (CIs), it is found that the proposed signal decomposition method based on the distribution law of sidebands can improve the accuracy of diagnosing the crack fault of the planet gear under the influence of the radial assembly error of the output shaft.

Weak Fault Diagnosis Method of Rolling Bearings Based on Variational Mode Decomposition and a Double-Coupled Duffing Oscillator

Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and VMD. The influence rule of system initial value on the response characteristics of a double-coupled Duffing system is studied, and the basis for its determination is given. The frequency of the built-in power of the system is normalized, and a variance evaluation standard for the output value of the double-coupled Duffing system for weak fault signals detection is established. In order to solve the interference problem of fault monitoring signals, VMD is proposed to pre-process the fault monitoring signals. The weak fault signal detection method proposed in this paper is tested and verified by simulation signals and rolling bearing fault signals. The results show that the method proposed in this paper can detect the weak fault signal with the lowest signal-to-noise ratio reduced by 2.96 dB compared with the traditional Duffing detection system, and it can accurately detect the early weak fault signal of rolling bearings.

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.

A Review of Flexible Acceleration Sensors Based on Piezoelectric Materials: Performance Characterization, Parametric Analysis, Frontier Technologies, and Applications

Acceleration sensors are tools for detecting acceleration and serve purposes like fault monitoring and behavior recognition. It is extensively employed in a variety of industries, including aerospace, artificial intelligence, biology, and many more. Among these, one of the major research hotspots and challenges is the development of low-energy, self-powered, miniature, mass-produced sensors. Due to its capacity to perceive human behavior and identify errors, the flexible acceleration sensor offers a distinct advantage in the use of flexible and miniaturized sensing systems. This review analyzes the current state of piezoelectric flexible acceleration sensors’ applications in the areas of sensitive materials, processing technology, and device structure and briefly summarizes the fundamental properties of these sensors. Additionally, it ends with a prognosis for the future growth of flexible piezoelectric acceleration sensors.

Efficient fault monitoring in wastewater treatment processes with time stacked broad learning network

Process monitoring models play an increasingly indispensable role in promptly differentiating faults within the wastewater treatment process to maintain a safe state. The accuracy and time overhead are crucial indicators in judging whether models results are excellent or not. In terms of accuracy, deep learning methods excel, because of their generalized approximation and strong learning capabilities that enable them to infinitely approach an ideal level of monitoring accuracy. Meanwhile, they also have large computational loads that generates significant time overhead, which poses challenges for real-time monitoring of the wastewater treatment process. Hence, a novel process monitoring method called Time-Stacked Broad Learning System (Time-SBLS) is developed to alleviate these concerns in an effective and accurate manner. Time-SBLS leverages the efficiency of the BLS while incorporating the residual mechanism and time lag idea to capture necessary nonlinear features and time correlation associated with the fault detection accuracy better. Furthermore, another advantage of Time-SBLS is its inherited efficiency from BLS regarding time overhead, which is minimized by utilizing the pseudo-inverse technique to calculate weight parameters. Experimental findings on the Benchmark platform reveal that Time-SBLS offers considerable advantages in terms of accuracy and network time overhead compared to other state-of-the-art approaches.

Rolling Average-Decision Tree-Based Fault Detection of Neutral Point Clamped Inverters

Multilevel inverters (MLI) with many switches, which are assets for EVs and other high and medium power applications are prone to switch faults. To ensure the reliable operation of MLI, simple, effective, and automatic fault monitoring and localization techniques are warranted. This paper focus on pole voltage rolling average (RA) based generalized open switch fault detection (FD) of neutral point clamped (NPC) MLI. The RA contributes advantage of smoothing the input, thus reducing the false alarm due to switching transients and standard deviation of averaged pole voltage serves as a good fault feature to distinguish between inner and outer switch faults for single switch fault case and the correct switch pair for double switch fault case. The proposed method based on pole voltage RA is fast and load-independent and uses decision tree tuned with random search as the classification tool for 12+1 and 78+1 classes, i.e., all possible chances of single and double switch fault combinations with training accuracy of 99% and 97.5% for three- level NPC. The paper also includes an analysis of how the DT classifier is affected by overfitting as the tree's depth increases.

Application of vibration signal detection in mine hoist fault monitoring system

Due to the harsh working environment and various types of faults in mine hoists, the implementation of conventional detection is relatively difficult. In order to improve the maintenance and fault detection efficiency of mine hoist, a portable vibration detection system is designed and applied in this paper, which can judge the type and characteristics of mechanical faults according to the different responses of vibration signals. According to the test conditions of the vibration system, the overall structure and functional composition of the hardware system are established. The key functional modules are designed, including signal conditioning module, main control module, vibration sensor and power management module. The main reasons that affect and produce the hoist fault and mechanical vibration are analyzed. The wavelet packet analysis method is proposed to realize the decomposition of low frequency and high frequency signals to improve the signal resolution of the whole frequency band in the frequency domain. The vibration test system has the function of real-time data transmission and control. The realization methods include WiFi and reserved RS485 communication interface, which can match the communication rate with the data sampling rate. The amplifying circuit of the electric signal is designed to effectively ensure that the output voltage of the charge amplifier is within the input range of the amplified ADC value. In order to verify the working effect of the system, the most common gearbox and bearing failures are taken as examples. The response signals of vibration test and diagnosis can be effectively obtained, and the fault types and characteristics can be accurately judged. Through confusion matrix analysis, the reliability of the system detection index is effectively verified. The research results show that the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect. It also can be known that different fault types can excite different vibration responses, especially at resonance frequencies. According to the verification, the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect.

Under Ground Fault Monitoring system Using Cloud Computing and IoT

An underground fault monitoring system project's major objective is to develop and put into use a system that can quickly identify and pinpoint faults in subterranean power cables. The system uses a network of sensors put along the cable's length to monitor and communicated at a on a variety of characteristics, including temperature, vibration, and current. For analysis to a central control unit. Advanced algorithms are used by the control unit to process the data and find any anomalies or potential problems. The system alerts the maintenance crew in real time when a fault is found, allowing them to swiftly find and fix the issue. Reducing the possibility of failure is the project's main objective in order to increase the availability and dependability of the electricity system.

Adversarial autoencoder concurrent projection to latent structure and its application

AbstractMultivariate monitoring plays an important role in process monitoring. Among the multivariate monitoring methods, the projection to latent structure method (PLS) has been most widely used in the fields of quality control and fault diagnosis. To improve the monitoring capability of traditional PLS methods in nonlinear and non‐Gaussian multivariate systems, this paper proposes an innovative multivariate monitoring strategy, which combines adversarial autoencoder (AAE) and the concurrent projection to latent structure method (CPLS). In the proposed strategy, the original data are mapped to the high‐dimensional space using the AAE method with the Gaussian prior distribution to realize data transformation. The mapped data are linearly divisible and approach the Gaussian distribution. Then, the projection with orthogonality is realized using the CPLS method. In addition, the reconstruction error and distribution properties are used as evaluation indexes of the high‐dimensional mapping performance. The corresponding monitoring strategy is established using the traditional statistical method based on the Gaussian distribution. Finally, the simulations are performed on the Tennessee‐Eastman process platform, and the results show that the proposed method could efficiently extract the principal components, especially in quality‐relevant fault monitoring.

A Graphical Representation of Sensor Mapping for Machine Tool Fault Monitoring and Prognostics for Smart Manufacturing

Abstract This paper introduces a graphical representation based on the fusion of several disparate standards to instantiate a sensor wrapper and sensing schema for fault delineation in machine tools and other manufacturing assets. Texas A&M researchers have already developed a sensor wrapper that aims to specify the sensor and the sensing suite based on a systematic consideration of the functionality (based on process dynamics) to derive the configuration and instantiation of a viable sensing suite. Adapting this scheme for real-world machines and manufacturing assets is challenging because of the complexity of the machine tool structure and the diversity of faults within its components. The presented graphical representation method is based on an ontological compliance with MTConnect and International Organization for Standardization/International Electrotechnical Commission standards, and the representation employs the graphical motifs pertaining to the fault tree framework. Such representation is essential for the delineation of failure modes associated with components of a machine tool, thereby making sensor-wrappers viable for the smartification of machine tools. The issues pruning the levels of the graphical representation based on the domain knowledge of the machine tool, and the many-to-many mapping between the components and sensors are discussed. The representation was applied in order to derive suitable sensing schemes for conventional machine tools, i.e., lathe and milling machines and a modern hybrid additive manufacturing machine.