Reliable Condition Monitoring Research Articles

Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application

Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application

Transportation bridge infrastructure asset condition monitoring using uncrewed aerial vehicles (UAVs)

The optimal performance of a road transportation network depends on the serviceability and reliability of bridges connecting the pavements. Bridge performance depends on several geotechnical assets among which abutments play a key role as they support the bridge span, and transfer the vertical and horizontal loads from the superstructure to the foundation. Soil abutments are designed to be stable against overturning and sliding and prevent differential settlement and excessive lateral movements. In the United States, 42 % of bridges are observed to be over 50 years old. Department of Transportation (DOT) agencies periodically monitor and assess the bridge conditions typically every two years. Due to the costs and the time involved in conducting inspections using traditional data collection tools, DOTs are exploring innovative technologies to conduct inspections and realize benefits in terms of access, cost, and safety. They have widely adopted the use of uncrewed aerial vehicles (UAVs) for structural health monitoring of bridges. A few studies also conducted 360⁰ bridge inspections using camera sensors mounted on UAVs in the recent past. In this current study, the US Federal Aviation Administration (FAA) guidelines for commercial use of drones were discussed and followed in field studies. Further, UAVs were demonstrated for conducting 360° bridge inspections and identifying the erosion near abutments using three-dimensional models. This paper presents the application of innovative technologies for geotechnical assessments of two bridges in the Alaska region, with a focus on assessing the need for rehabilitation measures. These new technologies have become essential for reliable condition monitoring post-disaster events as well. This approach is expected to benefit the agencies in managing the transportation geotechnical assets in a timely and cost-effective manner.

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.

Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network.

Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment.

Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis

The concept of a smart city is emerging to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technologies can be used as a means to promote sustainable and inclusive urban development. These technolgies include the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In a smart city, intelligent transportation systems (ITS) play a vital role in efficient traffic management. This paper explores the use of hybrid artificial intelligence techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT) as a powerful signal analyzer is applied for signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on Gram-Schmidt (GS) is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, the range of forecasting, the type of the day, the level of decomposition, and other factors all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.

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.

Probabilistic Prognosis of Wind Turbine Faults With Feature Selection and Confidence Calibration

With the ever-increasing expansion of the installed capacity of wind power generation, reliable condition monitoring for wind turbines (WTs) has become increasingly important. To this end, this paper proposes a probabilistic WT fault prognosis (WTFP) scheme to output reliable fault probabilities in addition to class predictions. First, a multivariate time series (MTS) based mutual information estimator (MIE) is developed to integrate with combinational optimization, selecting features that contain more beneficial temporal data patterns for WTFP. Then, a multi-fault prognosis model for WTs is trained based on the selected features and MTS learning network. Next, a confidence calibration (CC) post-processing module is appended to re-construct the mapping relation between network logits and posterior probabilities by minimizing negative log-likelihood, thereby calibrating confidence estimates to approximate the true correctness likelihood as much as possible while keeping the high accuracy of the trained MTS learning network. These components finally drive a comprehensive WTFP model. Test results on the field data of WT verify the efficacy of MTS-based MIE and CC, and show that the proposed probabilistic WTFP scheme can provide more reliable probability estimates, contributing to better decision-making.

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. This study presents a novel approach to road condition monitoring in smart cities through the development of an acoustic data processing and analysis module. It focuses on four types of road conditions: smooth, slippery, grassy, and rough roads. To assess road conditions, a microphone integrated road surface detector unit is designed to collect audio signals, and an ultrasonic module is used to observe the road depth information. The whole hardware unit is installed in the wheel rim of the vehicles. The data collected from the road surfaces are then analyzed using machine learning algorithms, such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrate the effectiveness of the proposed method in accurately identifying different road conditions. From these results, it was observed that the MLP provides better accuracy of 98.98% in assessing road conditions. The study provides valuable insights into the development of a more efficient and reliable road condition monitoring system for delivering secure transportation services in smart cities.

Revolutionizing condition monitoring techniques with integration of artificial intelligence and machine learning

The field of condition monitoring has made great strides in the development of innovative techniques that facilitate early detection and diagnosis of faults in equipment. Present article provides a comprehensive overview of condition-monitoring technologies, namely, ultrasound technique and radiography. By providing detailed insight into the strengths and weaknesses of mentioned techniques, present work highlights their suitability for various industrial applications and how artificial intelligence and machine learning can be integrated to form multimodal systems that improve overall performance. Additionally, it evaluates developments in artificial intelligence and machine learning that enable accurate and reliable condition monitoring systems.

Generating a Set of Reference Images for Reliable Condition Monitoring of Critical Infrastructure using Mobile Robots

The aim of this work is to reduce the amount of computational cost when monitoring the state of critical infrastructure objects using flying mobile robots equipped with correlation-extreme navigation system, based on minimizing the number of fragments of reference images. The goal is achieved by establishing a minimum permissible degree of correlation between the individual images, which form a set of reference images. The most essential result is substantiation of the approach to formation of a set of selective images based on scene correlation analysis and sufficiency of conservation of correlation connection of images in limits 0.6 ... 0.7. This reduces the amount of computation and extends the operating time of mobile robots while maintaining accuracy. The significance of the obtained results consists in the possibility of solving a complex task of forming a set of reference images, depending on the information content and stochastic conditions of sighting of critical infrastructure objects. The solution of this task will increase efficiency of critical infrastructure objects state control due to optimization of reference images number used in the monitoring process, increase operability, and provide high control reliability in stochastic sighting conditions. The novelty of the work lies in the fact that the method of process formalized description of forming a reference images set to ensure reliable monitoring of critical infrastructure facilities using flying mobile robots for various sectors of the economy, the practical application of which will ensure reliable control and their condition assessment.

An Unsupervised Vibration Noise Reduction Approach and Its Application in Lubrication Condition Monitoring

Accelerometers are sensitive devices that capture vibrational fault signatures from industrial machines. However, noise often contaminates these fault signatures and must be eliminated before analysis. A data-driven (DD) denoising algorithm capable of filtering useful vibrational fault signatures from background noises was derived in this study. The algorithm was first validated by comparing its denoised result with a numerically generated ideal signal with a known exact solution. The DD denoising approach reduced the Mean Squared Error (MSE) from 0.459, when no denoising was performed, to 0.068, indicating an 85.2% decrease in noise. This novel approach outperformed the Discrete Wavelet (DW) denoising approach, which had an MSE of 0.115. The proposed DD denoising algorithm was also applied to preprocess vibration data used for the real-time lubrication condition monitoring of the plastic injection molding machine’s toggle clamping system, thereby reducing false positive relubrication alarms. The false positive rates, when analysis was performed on the raw vibration and the DW denoised vibration, were 10.7% and 7.6%, respectively, whereas the DD denoised vibration yielded the lowest false positive rate at 1%. This low false positive rate of the DD denoised vibration indicates that it is a more reliable condition monitoring system, thereby making this technique suitable for the smart manufacturing industry.

Design Rules of Bidirectional Smart Sensor Coating for Condition Monitoring of Bearings

This paper reports a novel monitoring technique of bearings' bidirectional load (axial and radial) based on a smart sensor coating, which is screen printed onto the surface of a cross-shaped steel substrate. To ensure the accuracy and stability of measurement as well as the durability of the printed coating, the developed prototype is built according to design rules commonly used in electronic circuits. The finite element model (FEM) is used to predict the mechanical property of the tested substrate under either unidirectional or bidirectional loads. Regarding the output voltage of the piezoelectric sensor, experimental results are revealed to be well-corelated to the numerical simulation. It is pointed out that the output signal generated from the sensor (electrode) could be particularly affected due to the capacitive parasite coming from the conductive tracks (CTs). Such a phenomenon might be reduced by printing them on the dielectric layer rather than on the piezocomposite layer. The study also investigates a highly anisotropic shape of electrodes (rectangular instead of circle), indicating that the orientation of such electrodes (axial or radial) does affect the output measurement. To sum up, the high performance of a sensor network coating depends not only on the ultimate characteristics of its own materials, but also on its structural design. Such an issue has been rarely reported on in the literature, but is nonetheless crucial to achieving reliable condition monitoring of bearings, especially for multidirectional loads-a key signature of early failure detection.

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms.

Reliable fault diagnosis and condition monitoring are essential for permanent magnet synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be subject to various types of damage during operation. Magnetic damage is a unique fault of PMSM and concerns the permanent magnet (PM) of the rotor. PM damage may be mechanical in nature or be related to the phenomenon of demagnetization. This article presents a machine learning (ML) based demagnetization fault diagnosis method for PMSM drives. The time-frequency domain analysis based on short-time Fourier transform (STFT) is applied in the process of PM fault feature extraction from the stator phase current signal. Moreover, two ML-based models are verified and compared in the process of the automatic fault detection of demagnetization fault. These models are k-nearest neighbors (KNN) and multiLayer perceptron (MLP). The influence of the input vector elements, key parameters and structures of the models used on their effectiveness is extensively analyzed. The results of the experimental verification confirm the very high effectiveness of the proposed method.

Digital twin-driven vibration amplitude simulation for condition monitoring of axial blowers in blast furnace ironmaking

ABSTRACT Long-term reliable condition monitoring (CM) of blast furnace blowers is essential to avoid catastrophic failure. Due to variable working conditions, the predefined thresholds in current CM systems influence the accuracy of the monitoring process and can lead to misdiagnoses. In order to overcome this limitation, we propose a digital twin (DT)-based scheme to monitor vibrations found in blowers. Factors believed to impact the distribution of vibration amplitudes are analysed using data collected from a constant speed axial blower operating in an industrial commercial environment and, based on which, a machine learning-based adaptive amplitude simulation model is developed on our on-site private cloud computing platform. Outcomes reveal that different guide vane openings in the manufacturing process can cause changes in amplitudes. By integrating the newly-arriving sensor data, vibration amplitudes can be more accurately predicted in the virtual space. The gap between the simulated and actual value narrowed from ±5 µm to within ±3 µm, from which a dynamic threshold can be defined. The resulting DT model, coupled with the on-site private cloud computing platform, which alleviates the shortage of computational and storage capacity in steel plants, allows for a much more effective CM system.

State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

Sulfur Corrosion Mechanism in Power Transformers and Its Detection Techniques: A Review

Monitoring the progression of sulfur corrosion in insulating oils, particularly mineral oils, gives valuable information on the general health of the power transformers, thus preventing the possibility of catastrophic failures. The sulfur corrosion mechanism in insulating oils remains unclear although it is believed that this phenomenon is initiated by the presence of corrosive by-products of dibenzyl disulfide (DBDS) (i.e., mercaptans and elemental sulfur). A number of models have been developed that explain the chemical reactions, formation, and deposition of copper sulfide in power transformers. Irrespective of the sulfur corrosion mechanism, monitoring the progression of sulfur corrosion in power transformers is crucial before effective countermeasures can be implemented. The capability and limitations of standard and non-standard corrosion test methods based on the quality of the insulating oils and paper insulations are extensively reviewed in this article. This sets out the ultimate goal of searching for a reliable and quantifiable online condition monitoring tool to track the progression of sulfur corrosion in transformer insulating oils in real-time and facilitate timely remedial intervention.

MS-SSPCANet: A powerful deep learning framework for tool wear prediction

Reliable tool condition monitoring (TCM) system becomes increasingly critical to machinery efficiency, workpiece quality, and production cost in modern manufacturing industry. However, existing monitoring models have to face model generalization capacity or other problems, which significantly hampers their further application. Principal component analysis network (PCANet) is a promising interpretable deep learning model, and has been applied in image recognition. Nevertheless, the architecture still needs optimizing. In this paper, a novel multi-scale stacked sparse principal component analysis network (MS-SSPCANet) is proposed to predict tool wear. At first, time–frequency images are generated by short-time Fourier transform (STFT). Then, sparsity, multi-scale filters and multi-scale pooling layer are introduced to optimize PCANet structure. A hybrid pooling layer named GA-SPP, combining global averaging and spatial pyramid pooling layers, is applied to select features. And the selected ones from different filter scales are fused. Finally, support vector regression (SVR) is trained for tool wear prediction. Two milling experiments are conducted, and the vibration signal and corresponding tool wear data are acquired simultaneously. The following performance comparison experiments illustrate the better prediction capacity of the proposed model. Additionally, sparsity and two multi-scale operation (convoluting and pooling) are proven effective, and MS-SSPCANet also outperforms LeNet even after adding −6 dB noise.

A novel adaptive bandwidth selection method for Vold–Kalman filtering and its application in wind turbine planetary gearbox diagnostics

The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.

Oil-Immersed Power Transformer Condition Monitoring Methodologies: A Review

A power transformer is one of the most critical and expensive assets in electric power systems. Failure of a power transformer would not only result in a downtime to the entire transmission and distribution networks but may also cause personnel and environmental hazards due to oil leak and fire. Hence, to enhance a transformer’s reliability and extend its lifespan, a cost-effective and reliable condition monitoring technique should be adopted from day one of its installation. This will help detect incipient faults, extend a transformer’s operational life, and avoid potential consequences. With the global trend to establish digital substation automation systems, transformer online condition monitoring has been given much attention by utilities and researchers alike. Several online and offline condition monitoring techniques have been recently proposed for oil-immersed power transformers. This paper is aimed at providing a state-of-the-art review for the various condition monitoring technologies used for oil-immersed power transformers. Concept of measurements and analysis of the results along with the future trend of condition monitoring techniques are presented.

Implementation of Non-Destructive Condition Monitoring Techniques on Low-Voltage Nuclear Cables: II. Thermal Aging of EPR/CSPE Cables

Determining the aging state of low-voltage nuclear power plant cables using a nondestructive and reliable condition monitoring technique is highly desirable as the cables experience multiple aging stresses during the service period. This paper deals with the implementation and investigation of such nondestructive techniques, which can detect the overall aging state of low-voltage instrumentation and control (I&C) cables, which are subjected to accelerated thermal aging. The dielectric spectroscopy, extended voltage response, and polarization–depolarization current as nondestructive electrical aging techniques were used for the investigation purpose, while the elongation at break was also adopted as a mechanical measurement and for comparison. Prominent variations in the electrical parameters for the insulation and jacket were observed, whereas the elongation at break for both materials also decreased under thermal aging. Based on the electrical techniques, aging markers were selected that showed a strong correlation with the aging and elongation at break, proving the ability of the adopted electrical methods as a nondestructive condition monitoring technique.