Real-time Condition Monitoring Research Articles

Self-Organizing and Routing Approach for Condition Monitoring of Railway Tunnels Based on Linear Wireless Sensor Network.

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.

Micro-milling digital twin for real-time tool condition monitoring

Micro-milling is a promising manufacturing process due to its wide material compatibility with work materials and ability to fabricate complex micro features. However, the monitoring of tool condition is challenging because of the stochastic wear behavior these micro tools exhibit, which can drastically change due to instances of tool chipping. This paper presents the development of a high-fidelity digital twin of a micro-milling machine. High throughput data communication over OPC UA was used to communicate instantaneous sensor data to a high-performance computer. Using a trained deep learning model, we demonstrate real-time tool condition prediction over OPC UA. The deep learning model used was found to be in good agreement with post-machining tool wear measurements. The presented work highlights the importance of developing sound machine communication protocols and lays the foundation for integrating machine learning with micro-milling in real-time.

Detection and classification of surface defects on hot-rolled steel using vision transformers

This study proposes a vision transformer to detect visual defects on steel surfaces. The proposed approach utilizes an open-source image dataset to classify steel surface conditions into six fault categories namely, crazing, inclusion, rolled in, pitted surface, scratches and patches. The defect images are first subject to resizing and then fed into a vision transformer subject to different hyperparameter configurations to determine the most optimal setting to render highest classification performance. The performance of the model is evaluated for different hyperparameter configurations, and the most optimal configuration is examined using the associated confusion matrices. It was observed that the proposed model presents a high overall accuracy of 96.39 % for detection and classification of steel surface faults. The study presents a descriptive insight into the vision transformer architecture and in addition, compares the performance of the current model with the results of other approaches suggested for application in literature. Vision transformers can serve as standalone approaches and suitable alternatives to the widely used convolution neural networks (CNNs) by actuating complex defect detection and classification tasks in real-time, enabling efficient and robust condition monitoring of a wide range of defects.

An interpretable hybrid framework combining convolution latent vectors with transformer based attention mechanism for rolling element fault detection and classification

Failure of industrial assets can cause financial, operational and safety hazards across different industries. Monitoring their condition is crucial for successful and smooth operations. The colossal volume of sensory data generated and acquired throughout industrial operations supports real-time condition monitoring of these assets. Leveraging digital technologies to analyze acquired data creates an ideal environment for applying advanced data-driven machine learning techniques, such as convolutional neural networks (CNNs) and vision transformer (ViT) to detect faults and classify, enabling accurate prediction and timely maintenance of industrial assets. In this paper, we present a novel hybrid framework based on the local feature extraction ability of CNN with comprehensive understanding of transformer within a global context. The proposed method leverages the complex weight-sharing properties of CNNs and ability of transformers to understand the larger context of spatial relationships in large-scale patterns, making it applicable to datasets of varying sizes. Preprocessing methods such as data augmentation are used to train the model on the Case Western Reserve University (CWRU) dataset in order to increase generalization through computational efficiency. An average fault classification accuracy of 99.62% is accomplished over all three fault classes with an average time-to-fault detection of 38.4 ms. MFPT fault dataset is used to further validate the method with an accuracy of 99.17% for outer race and 99.26% for inner race. Moreover, the proposed framework can be modified to accommodate alternative convolutional models.

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads.

Model order reduction of thermal-dynamic coupled flexible multibody system with multiple varying parameters

Spacecrafts usually suffer from the thermal shock during operation, leading to large control error or even failure. At the same time, spacecrafts are often rigid-flexible coupled multibody systems with large degrees of freedom and multiple varying parameters. The real-time control and condition monitoring require effective order reduction methods. In this investigation, the displacement and temperature fields are discretized by the Absolute Node Coordinate Formulation (ANCF) to achieve unified description. The coupled thermal-dynamic reduced-order model (ROM) is established based on the Proper Orthogonal Decomposition (POD) method. For the purpose of further increase efficiency, a linearization method is introduced so that the calculation times of the elastic force can be significantly reduced. In order to predict the response of the thermal-dynamic coupled system with multiple varying parameters, the interpolation on Grassmann manifold is introduced to maintain the orthogonality of the basis. As verification and validation, three numerical examples are presented to show the feasibility and efficacy of the proposed method.

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.

Ensemble-based deep learning model for welding defect detection and classification

This study introduces an ensemble-based deep learning approach for monitoring and detecting submerged arc weld defects in weld beads and adjacent zones during Non-Destructive Testing (NDT). The methodology utilizes an open-source image dataset to categorize submerged arc weld conditions into three defect types: cracks, lack of penetration and porosity, and ideal welds (no defects). To enhance image analysis, we employ preprocessing and feature extraction in both spatial and frequency domains using segmentation techniques like grey level difference method (GLDM), grey level co-occurrence matrix (GLCM), discrete wavelet transform (DWT), Fast Fourier Transform (FFT), and texture analysis. Following image preparation, the preprocessed data undergoes training and testing in our proposed ensemble-based deep learning model. The model's performance is thoroughly assessed using key metrics such as precision, recall, F1 score, receiver operating characteristics (ROC) curve, and a confusion matrix, resulting in an impressive accuracy rate of 93.12% for detecting and classifying weld faults. In comparing our model to state-of-the-art techniques in existing literature, our ensemble-based deep learning approach consistently demonstrates a high fault detection and classification performance in the face of a simplistic MLP-based core architecture, ratifying the robustness of our feature ensemble approach. The study concludes that this model has significant potential for integration into current inspection systems, offering an efficient and robust solution for real-time condition monitoring of welds along weldment joints.

Anomaly detection of four-engine aircraft based on Parameter differences

Abstract In this paper, a real-time anomaly detection method combining GDN and Gaussian model is proposed for the real-time engine condition monitoring of four-engine aircraft. Firstly, the gas path parameters of the engine are selected and the difference of the gas path parameters is calculated to offset the influence of the external environment on the engine. Thirdly, the sliding time window method is used for real-time prediction, and the model prediction residuals are used for unsupervised anomaly detection. Finally, the Gaussian model is used to prune the anomalies detected by the GDN model to reduce the misjudgment of anomalies and improve the accuracy of anomaly detection. The experimental results show that the proposed method can detect 97.96% of the outliers in the test set and the accuracy reaches 89.47%, which is better than LSTM, AE, GDN, and Gaussian models alone.

Digital twin-based anomaly detection for real-time tool condition monitoring in machining

Real-time tool condition monitoring (TCM) has been emerging as a key technology for smart manufacturing. TCM can improve the dimensional accuracy of products, minimize machine tool downtime, and eliminate scraps and re-work costs. Digital twins offer new opportunities for real-time monitoring of machining processes, which can, in principle, take into account changes in machining processes and operating environments, help understand mechanisms of cutting tool wear, and improve the anomaly detection accuracy and fault diagnosis results. The present study exploits these potential advantages of digital twins and proposes a new digital twin-based anomaly detection framework for real-time TCM in machining. The framework of the digital twin consists of three parts: the physical product, the virtual product and data flow connections. Within this framework of the digital twin, the “physical product” represents the machining processes. The “virtual product” includes a real-time data-driven model representing the dynamic relationship between vibration data measured from machining processes as well as the model frequency features (MFFs)-based diagnostics for cutting tool anomaly detection. The “data flow connections” involve real-time measured vibration data and machine tool numerical controller (NC) signals providing real-time information on machine tool dynamics and various machining processes. The novelty is associated with an innovative integration of real-time data-driven modeling, MFFs extraction, and MFFs and machine tool NC signal-based tool wear diagnostics. This, for the first time, enables the concept of digital twins to be potentially applied to the TCM for complicated dynamic machining processes which, as far as we are aware of, has never been achieved before. Comprehensive field studies have demonstrated the effectiveness of the proposed digital twin-based TCM framework and its potential industrial applications.

Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring.

The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18 μJ of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security.

A Careful Examination of the Security and Traffic Effects Associated with the Vehicle Ad-hoc Network (VANET)

During recent years, vehicular ad hoc networks, or VANETs, have emerged as one of the most exciting and difficult study fields. VANET is regarded as a subset of MANET, or mobile ad hoc network, which is primarily used on automobiles. With the use of traffic data, VANET hopes to supply an advanced Intelligent Transportation System (ITS) with a wealth of information. Mention how daily traffic data collection helps with transportation planning, particularly with regard to intra-city communication. Two of the main issues with the existing traffic system are jams in traffic and accidents. Around the world, numerous people lose their lives or suffer severe injuries in traffic accidents each year. Human lives on the road are directly impacted by these issues. VANET can help to avert these mishaps and facilitate the efficient operation of daily traffic. Furthermore, VANET offers a plethora of intriguing applications, including real-time traffic condition monitoring, dynamic route scheduling, blind crossing prevention, safety, and more. In addition, there are certain drawbacks in terms of traffic and security. The absence of a centralised infrastructure in VANET is one of the main security issues. One of the most difficult jobs in the resultant decentralised and self- organizing VANETs is the administration of the wireless channel to make an efficient use of its capacity because there is no centralised infrastructure in charge of synchronisation and coordination of transmissions. VANET is primarily used to lower the risk of accidents in urban areas with a high volume and complexity of vehicles. We introduce the VANET Security R&D Ecosystem and examine traffic in this article. The four main components of the R&D ecosystem are end customers, government agencies, automakers, and university research. Every facet of VANET is covered in detail. Our study primarily focuses on the security and traffic within VANETs, including how data is safeguarded from vehicle-to-vehicle (V2V), vehicle-to-roadside infrastructure communications devices (V2I), access points, and other sources.

A closed-loop smart dressing based on microneedle and electrochemical micropump for early diagnosis and in-time therapy of chronic wound

Early diagnosis combined with in-time treatment is a promising strategy for chronic wound care, preventing wound deterioration and promoting healing. Different smart dressings have recently emerged to enable real-time wound status monitoring and offer active intervention. However, the drawbacks of existing smart dressings, including limited drug loading capacity and poor drug penetration into the deeper wound dermal and subcutaneous tissues, are yet to be addressed, resulting in unsatisfactory therapeutic effectiveness. In this study, a Microneedle-embedded closed-loop smart Dressing (MelD) is developed to simultaneously achieve chronic wound early diagnosis and in-time therapy. The simple impedance sensing electrodes are utilized to monitor wound impedance for chronic wound early diagnosis. Based on the sensing data, the drug delivery micropump in MelD is automatically activated for in-time therapy. By integrating micropump electrodes with a drug reservoir coupled-microneedle array, a practice solution for delivering enough drug fluid into deeper wound dermal/ subcutaneous tissues is developed. Compared to untreated diabetic mice, MelD, delivering sufficient growth factor to the wound and promoting drug entry into deep tissue layers, induces faster wound healing in treated mice. Meanwhile, MelD facilitates tissue formation and nascent collagen deposition. The enhanced drug storage dosage, improved drug penetration and closed-loop operation of Meld enable the first successful attempt of ensuring sufficient and efficient drug delivery by miniaturized all-in-one smart dressing, expanding the usage of closed-loop chronic wound care from laboratorial demonstration on limited kinds of superficial wounds to a wider range of wound types.

Hot rolled steel surface defect detection and classification using an automatic ensemble approach

This study introduces an ensemble-based Deep Neural Network (DNN) model for detecting defects on steel surfaces. The method suggested in this study classifies steel surface conditions into six possible fault categories, namely, crazing, inclusion, rolled in, pitted surface, scratches, and patches. The images undergo preprocessing and extraction of features in spatial and frequency domains using image segmentation techniques such as grey level difference method (GLDM), fast Fourier Transform (FFT), grey level co-occurrence matrix (GLCM), texture analysis and discrete wavelet transform (DWT). The ensembling of image features into a fused feature pool is carried out after the preprocessing of input images that are provided as input to a light-weight neural network model for training and testing. The performance of the model is comprehensively evaluated via an ablation study both before and after ensembling. In addition, the model capability is effectively analyzed using receiver operating characteristics (ROC) curve, confusion matrix from which classification accuracy of the model could be obtained and other parameters including precision and f1-score. It was observed that the proposed deep learning network presents phenomenally high accuracy of 99.72% for detection and classification of steel surface faults. This result was found to be superior when compared with the performance of the same neural network over each feature type individually. This study also compares the classification results of the model built based on the ensembled feature set with the results of various other classification approaches available in literature. The ensemble-based model could potentially be integrated into existing inspection systems for real-time, efficient and robust condition monitoring of steel surfaces.

IoT Based Monitoring and Maintenance of Highway Bridges using WSN

This paper presents a WSN-based IOT-based bridge monitoring system. The automated real-time bridge health monitoring system has been made possible by sophisticated modifications to sensor technology; this system will aid in disaster management. The technology used in this development is a wireless sensor network (WSN). This system uses a variety of sensors to continually monitor the state of the bridge. These sensors include an accelerometer to identify jerks in the bridge, a vibration sensor to identify vibrations occurring on the bridge, a flex sensor to identify bends in the bridge, and a water level sensor to identify the water level. To determine the weight on the bridge, use the load sensor. The barrier is detected by an IR sensor. Only the buzzer will activate when these are high. Additionally, a microcontroller processes the data from many sensors and transmits it to a server and management system so that real-time bridge condition monitoring via a mobile device using the GSM model is possible

A Wiener-process-inspired semi-stochastic filtering approach for prognostics

Semi-stochastic filtering method has been termed as one of the typical methods for predicting remaining useful life (RUL). Despite of its simple and direct framework, selecting or determining the conditional distribution of the condition monitoring (CM) measurements given the RUL is a challenging task remaining to be solved. To address this issue, this paper presents a novel Wiener-process-inspired semi-stochastic filtering approach for prognostics. First, the relationship between the degradation monitoring data modeled by Wiener process and the RUL of the system is explored. Inspired this relation, the conditional distribution of the CM measurements give the RUL is directly established and used as the observation equation in semi-stochastic filtering based prognosis method, and the parameters in Wiener process and semi-stochastic filtering model are estimated by the maximum likelihood estimation method. Then, the RUL of the concerned in-service system can be predicted with a probabilistic distribution based on the constructed state equation and the estimated model parameters using the CM data to date. To do so, the proposed prognosis method has an ability to integrate the historical data and the real-time CM data of the in-service system. Finally, the developed method is validated by the wear data of milling cutters and Lithium-ion button cells.

Digital twins and dynamic NFTs for blockchain-based crowdsourced last-mile delivery

In this paper, we propose a blockchain-based crowdsourcing framework leveraging Digital Twins (DTs) and dynamic Non-Fungible Tokens (NFTs) for a transparent last-mile delivery. Existing works propose package delivery through crowdsourced workers managed by centralized or blockchain-based platforms. While the centralized platforms suffer from data security and reliability vulnerabilities, the blockchain-based platforms lack real-time and transparent package status monitoring. The availability of immediate access to status updates and early warnings regarding package storage conditions greatly contributes to ensuring the safe delivery of sensitive packages and preventing spoilage or further damage. As a solution, we propose a decentralized crowdsourcing platform for last-mile delivery utilizing Ethereum smart contracts and DTs for both the packages and the workers. The contracts are leveraged to facilitate interactions between the requesters and workers, and securely execute the task allocation mechanism, which relies on a computed Quality-of-Service (QoS) metric. The package DTs are proposed to oversee the package status and promptly alert workers’ DTs when delivery conditions are violated. To ensure data integrity and prevent malicious alterations, the DTs’ data is securely stored using NFTs. Upon task completion, a calculated Quality-of-Delivery (QoD) acts as a feedback loop in updating workers’ reputations. The proposed allocation mechanism is benchmarked against distance-based and preferences-based allocation mechanisms where our framework improves the average QoS by 76%, average QoD by 22%, and quality of selected workers by 11%. In addition, the DTs are implemented to verify alert generation through a use case. We provide a detailed cost analysis of deployed smart contracts, along with insights into current limitations and proposed mitigation strategies.

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT).

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Dirichlet probability navigated fault detection via key-group memory auto-encoder under non-stationary working conditions

Real-time condition monitoring is the foundation of prognostics and health management (PHM) for mechanical systems. Constraint by extravagant cost and insurmountable obstacles to simulate real faults in actual working conditions, establishing fault detection models with historical normal data is the most promising way. Meanwhile, non-stationary fault detection is rarely studied in the literature but fits most in real working conditions. Thus, an innovative method named KGMem-DirAE is proposed to conduct real-time fault detection under non-stationary working conditions using normal data only. In the training stage, normal patterns with different semantics are recorded in the developed key-group memory module (KGMem) combined with auto-encoder. Considering the matching probabilities of encoded latent features and recorded memory units (normal patterns), a statistical anomaly score defined by aggregated negative log-likelihood of Dirichlet distributions is proposed to detect incipient failures. In order to deal with time-varying working conditions and capture fault degradation trends concurrently, normalized time-frequency maps containing fault-evolving information are obtained from vibration data. Both widely studied non-stationary and stationary run-to-failure data sets are deeply researched using the proposed KGMem-DirAE and the experimental results proved its superiority against other comparative anomaly detection methods.

Accurate Time-Efficient Thermal Modeling and Discretization Methodology Applied to Electric Machines

The increasing power density of electric machines for electric vehicles necessitates accurate thermal analysis in design and real-time condition monitoring through simulations with computation times not exceeding one minute. Existing thermal models are either accurate and complex while requiring large computation times or they are fast but lack accuracy. To address these challenges, a novel hybrid modeling methodology is proposed, combining 3D lumped parameter thermal networks with thermal resistances extracted from 2D finite element models (FEM) to reduce computation demands while preserving accuracy. Further, a methodology is introduced to define the thermal network discretization for a desired threshold accuracy while minimizing computation time. Analysis of this discretization methodology demonstrates that the desired accuracy can be reliably achieved with limited computational effort. Verification of the modeling methodology against 3D FEM in OpenFOAM shows key component temperature deviations of less than 0.67 °C in steady-state simulations and 0.75 °C in transient simulations, comparable to the 3D FEM uncertainty of 0.39 °C. The computation times for steady-state and transient simulations are 18.3 s and 43.5 s respectively compared to 44 h and 175 h for 3D FEM. This highlights the time efficiency of the proposed methodology while maintaining accuracy for key component temperatures.