Condition Monitoring Of Equipment Research Articles

Segmentation and Tracking Based on Equalized Memory Matching Network and Its Application in Electric Substation Inspection

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.

Application of optimized sparse encoding algorithm in data compression

Data transmission is crucial in the process of equipment monitoring. The compression algorithms are adopted to reduce the amount of data transmission. When sparse encoding algorithms are used for this purpose, despite extensive attempts to improve their performance, some problems still remain. The main issues pertain to: (1) Dictionary construction − As practical dictionary better matches the input signal, such a dictionary must be efficiently established; 2) The setting of sparsity value − It is difficult to determine the size of sparsity values when prior knowledge is insufficient (when the parameter settings are too large, the cost of data transmission increases considerably, while excessively small values can result in poor reconstruction accuracy); and (3) How to implement engineering applications of data compression based on sparse encoding. To address these issues, in this work, an optimized sparse encoding algorithm is proposed by combining the non-negative Online Dictionary Learning (ODL) with the optimized Orthogonal Matching Pursuit (OMP) algorithm. First, a non-negative ODL algorithm is adopted for dictionary learning based on the training dataset to obtain an effective dictionary. Next, the optimized OMP algorithm is used to obtain the sparsity and the sparse coefficient matrix. Further analyses confirm that the reconstructed signal has a small reconstruction error. Finally, A lossy compression framework is proposed for the compressing equipment condition monitoring data using sparse encoding algorithms. Compared with the compression algorithms based on compressive sensing and DCT, the average compression ratio can reach 42.7 when the reconstruction accuracy is similar. In terms of comprehensive compression performance, the quality score is also higher compared to other algorithms.

A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring.

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

Research of IoT Technology Based Online Status Monitoring on Hydropower Station Equipment

The rapid proliferation of the Internet of Things (IoT) has revolutionized the field of status monitoring for electrical equipment such as hydropower station equipment, offering enhanced efficiency and reliability in maintenance and operations. In this paper, we investigate the utilization of IoT transmission technology enhanced by multiple relays, denoted as $M$ relays, to augment the monitoring of hydropower station equipment. To further optimize the performance of the system, we employ partial relay selection, commonly referred to as selection combining. This study delves into the analysis of the system performance by deriving analytical data rates, with a focus on quantifying the benefits of employing partial relay selection in IoT transmission for electrical equipment status monitoring. Our analytical approach enables a comprehensive evaluation of system efficiency, considering factors such as data rate, reliability, and power consumption. Through our analysis, we aim to provide valuable insights into the trade-offs and advantages of incorporating partial relay selection into IoT systems, ultimately assisting in the development of more effective and efficient solutions for status monitoring on electrical equipment. By examining the impact of $M$ relays and partial relay selection on IoT transmission technology, this research contributes to the ongoing efforts to enhance the reliability and robustness of status monitoring for electrical equipment, ultimately advancing the capabilities of IoT-based solutions in the context of electrical systems and equipment maintenance.

Interface Control and Status Monitoring of Electronic Information Equipment based on Nonlinear Data Encryption

An advanced electronic information equipment interface control and status monitoring system is proposed to ensure the fairness, objectivity, and security of information while identifying responsibility for traffic accidents. Through an in-depth analysis of the system's security requirements and the current landscape of information security technology, a robust security strategy is developed for each crucial system stage. A PC-based platform is developed for efficient data acquisition, secure processing, reliable transmission, and fortified storage, focusing on implementing nonlinear data encryption methods. Performance evaluation of the system involved rigorous testing using files ranging from 3MB to 10MB. The results of the proposed system revealed a significant improvement in the system's overall speed and efficiency, showcasing an average performance enhancement of one quarter compared to the original platform. The proposed system demonstrated an impressive 15% to 30% increase in processing speed, establishing its capability to ensure data integrity protection during information transmission, facilitate accurate identification of data recording equipment post-accident, and safeguard the security of stored data. The developed electronic information equipment interface control and status monitoring system effectively addresses critical challenges associated with ensuring data integrity and security in traffic accident investigations.

Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.

Design and use of a wireless temperature measurement network system integrating artificial intelligence and blockchain in electrical power engineering.

This work aims to investigate the potential fire hazard stemming from the overheating of power equipment. The advent of the artificial intelligence era has facilitated the fusion of blockchain and Internet of Things (IoT) technologies. This work delves into the technical standards for IoT equipment monitoring and smart grid communication, and the IoT environment of power grid equipment. This work introduces a temperature monitoring network tailored for IoT wireless power equipment suitable for the power environment, and conducts system debugging in the power laboratory. The findings affirm that the temperature out-of-limit alarm testing has met the required criteria, confirming the system's ability to issue timely warnings when temperatures breach a predefined threshold, effectively avoiding high-temperature misfires. This work fully harnesses the secure and user-friendly operation of smart blockchain and the wireless sensing technology of the IoT to realize online monitoring and remote temperature measurement of the power system. It can effectively prevent equipment from overheating and damage, and promote the development of equipment condition monitoring technology in electric power engineering.

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A bibliometric analysis on condition monitoring of machinery using IR Thermography

In order to extend the life of machinery, condition monitoring of equipment has become crucial in every industry or power plant. IR Thermography is a strong tool for condition monitoring since it can detect flaws earlier than other techniques. The most important technique for condition monitoring is infrared thermography. Machine condition monitoring has evolved along with machine maintenance, from manual maintenance through approaches involving the use of sensors to the now under discussion methods and techniques centered on shared automation, data processing, and exchange. The purpose of this paper is to give a brief summary of the academic work that has been done on using IR thermography to monitor the status of machinery. In order to present an up-to-date, content-based analysis that includes bibliometric performance analysis and systematic literature evaluation, this paper evaluates the body of current literature. The writers may concentrate on identifying the most important researchers, journals, and publications, as well as the most significant and influential researchers, thanks to the mix of literature review methodologies used. These conclusions have prompted the authors to outline research trends pertinent to the studied domain and, in addition, to identify potential future research goals from engineering viewpoints.

A methodology for low-frequency rotational energy harvesting with synergistic effects of centrifugal softening and passive adaptive barrier reduction

Energy harvesting in rotating environments is significant to realize the condition monitoring of mechanical equipment. In this paper, to accommodate the low-frequency rotating environment, a piezoelectric energy harvester with two magnetic coupled cantilevers perpendicular to each other is designed. The harvester exhibits the magnet-induced nonlinearity, centrifugal softening effect and passive adaptive barrier reduction effect. The influences of these characteristics on the dynamic performance are investigated comprehensively by establishing the electromechanical coupling equations in a rotational coordinate system through the extended Hamiltonian principle. The correctness of the mathematical model and simulation analysis is verified by experiments. Compared with single cantilever based harvester, the system output voltage is boosted by more than 10.56 times over the whole test frequency band (0.6–4 Hz). The significant enhancement of the energy scavenging capability proves its potential for low-frequency rotating applications.

Lamb-Waves-Based Sparse Distributed Penetrating Communication via Phase-Position Modulation for Enclosed Metal Structures

Sensor nodes have been widely used for mechanical equipment condition monitoring, especially for enclosed metal structures. In these cases, it is important to achieve communication between the sensor nodes inside the enclosed metal structures and the outside receiver node. Conventional wired communication using cables will affect the structural integrity, and wireless communication using electromagnetic waves will be shielded by metal structures, so neither is desirable. Motivated by these issues, this study proposes a Lamb waves-based sparse distributed penetrating communication (SDPC) system using the propagation characteristics of Lamb waves considered harmful in previous studies to accomplish wireless simultaneous data communication. Combining phase shift keying and pulse position modulation mechanisms, a phase-position modulation (P-PM) technology is proposed to improve the communication rate. To overcome the intersymbol interference (ISI) problem caused by the propagation characteristics of Lamb waves, a phase-position sparse reconstruction (P-PSR) strategy is proposed for demodulation, which is more convenient and accurate compared with the conventional cross-correlation method. It should be noted that the maximum bit rate without bit error reaches 830.48 kbps via P-PM parameter optimization, which is 8.3 times higher than that of on-off keying modulation approach under the same experimental conditions. Finally, distributed communication experiments and simulations further verify the high accuracy, efficiency and strong robustness of the proposed SDPC system, which can meet the communication requirements of enclosed metal structures.

Rotational Speed Measurement of a Shaft Using Infrared Sensor with NI Data Acquisition system and LabVIEW

Rotational speed measurement is the process of determining the number of rotations completed in a unit time. Generally, it is measured in Hertz, radians per second and rotations per minute (RPM). Rotational speed measurement is very critical parameter in condition monitoring, speed control, and supervision of rotational equipment like generators and turbines. This paper presents an alternate method of measuring the speed of a shaft using one of the cheapest infrared sensors available in Nepalese market. The principle of measurement of speed is on the basis of reflected and absorbed infrared light which is acquired using data acquisition unit of National Instrument. The signal processing has been carried out in a LabVIEW program and speed of shaft has been verified using the other optical sensors.

Power supply station equipment status monitoring and evaluation system based on wireless network technology

With the continuous development of the power industry and the acceleration of the process of intelligence, monitoring and analyzing the status of power supply equipment is crucial for ensuring power supply and safety. Traditional power monitoring systems still have certain problems in data transmission. Using wireless network technology (WNT) to detect and analyze the status of power supply station equipment can not only achieve real-time monitoring and remote control, improve equipment operation and management efficiency, but also achieve automated monitoring and analysis of equipment status, providing technical support for the intelligence of power supply station equipment. In order to verify the effectiveness of the WNT-based power supply station equipment status monitoring and analysis system, a comparative experiment was conducted with traditional wired network monitoring methods. The final experimental results show that the average feedback time for testing the WNT power supply station equipment status monitoring and analysis system is 7.71 s, which is 38.22 s shorter than traditional methods. The average fault location accuracy is 95.98 %, which is 35.91 % higher than traditional methods. The average management efficiency is 92.88 %, which is 34.71 % higher than traditional methods. Through testing data, it can be concluded that the power supply station equipment status monitoring and analysis system based on WNT has greater advantages than traditional power equipment monitoring methods.

Equipment Condition Monitoring of Multiple Oxide-Nitride Stack Layer Deposition Process

Equipment Condition Monitoring of Multiple Oxide-Nitride Stack Layer Deposition Process

Multi-Channel Dual-Mode Oil Multi-Pollutant Detection Sensor

In order to realize the lubricant fluid condition monitoring of ships and offshore engineering equipment, a multi-channel, dual-mode oil multi-pollution detection sensor is proposed and fabricated. The sensor has three detection channels connected via tee tubes, as well as two different detection modes, inductive and capacitive, respectively. In comparison to the traditional sensor, this sensor not only has the ability to distinguish and identify a diverse range of pollutants, but it also experiences an 11-fold increase in its volume of flow, resulting in a significant enhancement in detection efficiency. The mechanism of the inductive and capacitive modes for the differentiated detection of multiple pollutants is elucidated through theoretical analysis. The performance of the sensor is investigated using the constructed experiment platform. The experimental results show that the sensor can realize the simultaneous detection of metallic and non-metallic contaminants in lubricating oil fluids. It can detect the smallest iron particle size of 54 μm, the smallest copper particle size of 90 μm, the smallest water droplet size of 116 μm, and the smallest air bubble size of 130 μm. A novel approach for achieving ship and marine engineering equipment health monitoring and fault diagnosis is presented in this study.

Improved OAKR approach to condition monitoring of rotating machinery

Faults in main subsystems or components of a rotating machine often causes unscheduled shutdown, which may lead to not only huge economic losses, but also safety accidents. As an important part of intelligent maintenance, condition monitoring becomes a powerful tool in reducing maintenance costs through automatic fault alarming, thereby reducing potential downtime while improving system safety and reliability. An optimized auto-associative kernelregression (OAKR) model has been proposed recently and demonstrated as a promising tool for condition monitoring of various turbomachines, which is independent of fault mode and machine type. However, the fault identification accuracy of this approach largely relies on data quality in practical applications. Data incompleteness, parameter variation and system complexity often result in the inaccuracy of fault alarming for complicated rotating machinery. This paper proposes an improved OAKR method to address these issues, including utilizing wavelet packet Bayesian thresholding method (WPB) to reduce noise in the raw multivariate data, developing the Manhattan distance to calculate the sample similarity, and constructing a multivariate health index based on Multivariate Permutation Entropy to identify potential faults in equipment condition monitoring. Parametric analysis and a comparison study with original AAKR and OAKR methods by using the actual data of a gas turbine are conducted to illustrate the effectiveness and feasibility of the proposed methodology.

Demonstration of Sensor Monitoring of Lubricants

Due to the spread of carbon neutral, the effective use of lubricant has been drawing attention. Since the main component of lubricant is petroleum-derived hydrocarbon oil, reducing the amount used by 1 kg will reduce CO2 by approximately 3 kg. The value of CO2 reduction is very important. In order to reduce the amount of lubricant used, there is a movement to reduce the frequency of lubricant exchange or continue to use lubricant without exchanging it. However, it is known that lubricant-induced mechanical failures occur. For this reason, equipment condition monitoring using oil sensors has been spread. The color of the lubricant, also called machine blood, indicates the condition of the machine. The oil sensor measures contamination, which has a fatal effect on machine failure, and oxidation degradation, which is related to the performance of lubricant and the machine failure. Contamination includes water and wear debris, and oxidative degradation includes consumption of additives and oxidation of base oil. By digitizing the hue of wind turbine gear oil through color diagnosis using an oil sensor, the oil contamination and degradation is identified. Additives in the gear oil were quantified by liquid chromatography-mass spectrometry, and it was found that the color change of gear oil was highly correlated with the depletion of the extreme pressure additive. It is known that the depletion of the extreme pressure additive is correlated with the useful life of the gear oil. Using the technique, the remaining life diagnosis of the gear oil was shown. Demonstration in the gearbox with the oil sensor was succeeded by avoidance of the effects of air bubbles in the gear oil.

Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

In an actual industrial scenario, machines typically operate normally for the majority of the time, with malfunctions occurring only occasionally. As a result, there is very little recorded data on defects. Consequently, the fault diagnostic dataset becomes imbalanced, with a significantly lower number of fault samples compared to normal samples. Furthermore, with the rapid development of the manufacturing industry, the increasing complexity of machines and equipment leads to various challenges in collecting fault data, such as noise, within-class imbalance, multi-class imbalance, and time series imbalance. It is worth noting that this study is the first to comprehensively summarize these four specific challenges. Therefore, addressing these issues has become a critical research focus and a pain point in the field of fault diagnosis, and numerous solutions have emerged. This study provides a comprehensive overview of these solutions at three levels: data preprocessing, feature extraction, and classifier improvement. It also describes the applications of imbalanced data classification methods, including pure resampling techniques, as well as sampling techniques that combine resampling algorithms with feature extraction and classifier improvement in industrial scenarios. Finally, we summarize the challenges facing imbalanced data classification research and suggest potential directions for future studies.

Unraveling Induction Motor State through Thermal Imaging and Edge Processing: A Step towards Explainable Fault Diagnosis

Equipment condition monitoring is essential to maintain the reliability of the electromechanical systems. Recently topics related to fault diagnosis have attracted significant interest, rapidly evolving this research area. This study presents a non-invasive method for online state classification of a squirrel-cage induction motor. The solution utilizes thermal imaging for non-contact analysis of thermal changes in machinery. Moreover, used convolutional neural networks (CNNs) streamline extracting relevant features from data and malfunction distinction without defining strict rules. A wide range of neural networks was evaluated to explore the possibilities of the proposed approach and their outputs were verified using model interpretability methods. Besides, the top-performing architectures were optimized and deployed on resource-constrained hardware to examine the system's performance in operating conditions. Overall, the completed tests have confirmed that the proposed approach is feasible, provides accurate results, and successfully operates even when deployed on edge devices.

CBAM-YOLOv5: A Promising Network Model for Wear Particle Recognition

The intelligent recognition technology for ferrography images is one of the important methods for diagnosis fault and state detection of machines. In allusion to these questions for the influences of wear particle images’ blurring, background intricacy, wear particle overlapping and lack of light, and others which lead to be the reason for the difficulty of achieving accurate identification, missed detection, and false detection, an intelligent recognition algorithm for ferrography wear particle based on convolutional block attention module (CBAM) and YOLOv5 is proposed. Firstly, it needs enhancement to improve contrast for ferrography wear particle images and lower background interference by adaptive histogram homogenization algorithm. Then, under the framework of YOLOv5 algorithm, the depthwise separable convolution is added to improve the detection speed of the network, and the detection accuracy of the entire network is improved by optimizing the loss function. Moreover, increase weight ratio on wear particle in images by adding a convolution block CBAM model and increase feature representative capability in detection network with YOLOv5 algorithm detection network, which can improve detection accuracy for wear particle. Finally, compare the algorithm with the three classical homologous series object detection algorithm. The experimental results show that the detection accuracy of the model can reach 96.7%, and the detection speed is 32 FPS for the images with a resolution of 1280 × 720 . It can be developed and applied to the fault diagnosis and condition monitoring of mechanical equipment.