Continuous Condition Monitoring Research Articles

Optical Measurement System for Monitoring Railway Infrastructure—A Review

Rail infrastructure plays an important role in fulfilling the demand for freight and passenger transportation. Increases in traffic volume, heavier axles and vehicles, higher speeds, and increasing climate extremes all contribute to the constant strain on the infrastructure. Due to their major importance in the transportation of people and freight, they are subject to continuous condition monitoring. This is an essential requirement for the selective planning of maintenance tasks and ultimately for safe and reliable operation. Various measuring systems have been developed for this purpose. These must measure precisely, quickly, and robustly under difficult conditions. Whether installed from mobile or stationary platforms, they have to cope with a wide range of ambient temperatures and lighting conditions, harsh environmental influences, and varying degrees of reflection. Despite these circumstances, railway operators require precise measurement data, high data densities even at high traveling speeds, and a user-friendly presentation of the results. Photogrammetry, laser scanning, and fiber optics are light-based measurement methods that are used in this sector. They are able to record with high precision rail infrastructure such as overhead contact systems, clearance profiles, rail tracks, and much more. This article provides an overview of the established and modern optical sensing methods, as well as the use of artificial intelligence as an evaluation method, and highlights their advantages and disadvantages.

Condition-based Predictive Maintenance as an Efficient Strategy for Industrializing Additive Manufacturing Technology

Objective: Traditional manufacturing has several issues to determine a general maintenance strategy. Additive Manufacturing (AM) is a special type of fabrication, which possesses different issues, especially uncertainty failure cases. This forms a big obstacle to becoming an industrialized production strategy. The objective of this work is to provide the most suitable maintenance strategy in order to reduce the likelihood of failure that can help in industrializing the AM technology. Methods: Among the different maintenance strategies, Predictive Maintenance (PdM) became widely treated in academia and industry. According to the methods used for detecting the failure signs, it can be classified into two types: Condition-Based Predictive Maintenance (CBPdM) and Statistical-Based Predictive Maintenance (SBPdM). The use of the last type highly depends on available and accessible data. However, CBPdM depends on only periodic or continuous condition monitoring tools for detecting the failure signs. Results: The existence of various types of failure cases yields to a big difficulty to predict failures. In addition, because of insufficient data, SBPdM cannot be selected as a suitable maintenance strategy for additive manufacturing. According to the presented examples, the CBPdM can be considered here as the best maintenance strategy for AM. Conclusion: CBPdM strategy is the best compromise between cost and applicability where there is not enough data. This selected maintenance approach represents an efficient tool in industrializing AM technology.

Coupling vibration mechanism of multistage blisk-rotor system with blade crack

This study aims to address the issue of vibration modeling and coupling vibration mechanism analysis of multistage blisk-rotor (MBR) systems with blade cracks. First, a comprehensive coupling vibration model of the MRB system containing the flexibility of shaft and blade, the swing motion of offset blisk structure, and the nonlinear effect of blade breathing crack is developed. Then, the coupling vibration mechanism of the MRB system is systematically investigated through analytical and numerical analysis, based on which the vibration indicators for the presence of blade cracks are extracted. At last, the effects of blade crack and operating condition parameters on the coupling vibration of the MRB system are comprehensively analyzed. The comparative results indicate that the crack depth and aerodynamic force may significantly affect the crack indicators in shaft bending and torsional vibrations, while the disc unbalance mainly affects the crack indicators in shaft bending vibrations. It is also suggested that the BHM and crack detection of rotating blades should comprehensively consider different crack indicators and perform continuous condition monitoring to obtain more robust and accurate results.

Alleviating confirmation bias in perpetually dynamic environments: Continuous unsupervised domain adaptation-based condition monitoring (CUDACoM)

MotivationDeep learning (DL) has revolutionized condition monitoring (CoM) in mechanical systems by reducing manual signal processing. However, DL's industrial integration is limited due to low robustness against distribution shifts. Existing CoM approaches focus on adaptating to one-time distribution shift. We introduce Continuous Unsupervised Domain Adaptation-based CoM (CUDACoM), tackling continuous distribution shifts in systems under perpetually dynamic conditions. MethodologyCUDACoM mitigates confirmation bias, detrimental in long-domain sequences, by introducing two novel strategies (1) Fresh Initialization and (2) In-Domain Pseudo Labeling. Fresh Initialization maintains high model plasticity, while In-Domain pseudo-labeling improves pseudo-label accuracy, enhancing model adaptability. These strategies reduce confirmation bias, crucial for robust self-training, making CUDACoM ideal for long-sequence domain adaptation in perpetually dynamic environments. ResultsCUDACoM outperforms state-of-the-art (SOTA) adversarial and self-training approaches. Validated through two practical case studies: a 200% (RPM) change and gradual sensor degradation across 40 noise levels. These challenging case studies show stronger data shifts than the commonly used standard benchmarking datasets. The second case is especially novel, formulating robustness to noise as a domain adaptation problem. CUDACoM achieved a test accuracy of 0.937 in the RPM case (vs. SOTA's 0.770) and 0.849 in the sensor degradation case (vs. SOTA's 0.751). ImpactThis study addresses the overlooked challenges of employing DL for CoM in perpetually dynamic environments, particularly, the confirmation bias. With CUDACoM's computational efficiency, we provide a practical solution that enhances reliability by facilitating the integration of robust DL into industrial CoM systems.

Multi-parameter-based fuzzy logic diagnostic system for health assessment of power transformer

ABSTRACT Power transformers are the most valuable assets in the power system. An appropriate asset management is therefore essential for ensuring a reliable power supply to the utility managers. This is accomplished through continuous transformer condition monitoring. The transformers’ asset management involves a severe investigation into its insulation health index. It is determined by utilising several diagnostic parameters of transformer insulation. In this context, a novel fuzzy logic-based health index model has been proposed in the present paper to find the overall health condition of transformers. This model utilises the results extracted from several important diagnostic tests of transformer insulation, such as furan analysis and dissolved gas analysis. The validity of the proposed fuzzy logic model has been tested using real data from 200 transformer samples. These samples are collected from different-rated transformers of the Himachal Pradesh State Electricity Board (HPSEB), India. Further, the efficacy of the proposed fuzzy logic model has been validated against the expert model presented in literature. It is revealed from the comparison that the accuracy of the present proposed model is 92%. Also, it is observed that the proposed fuzzy logic model is very effective in interpreting dissolved gases and the other diagnostic test results of the transformer.

A Study of Adaptive Threshold Based on the Reconstruction Model for Marine Systems and Their Equipment Failure Warning

To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.

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.

Application of fuzzy logic in multi-sensor-based health service robot for condition monitoring during pandemic situations

PurposeThe purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.Design/methodology/approachIt becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input Xj (j = 1, 2, ………. s), and output yi. The inputs and output variables are formed by the membership functions µAij(xj) and µCi(yi) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.FindingsThe fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.Originality/valueThe novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.

Designing Telemedicine for Older Adults With Multimorbidity: Content Analysis Study.

Telemedicine is a potential option for caring for older adults with multimorbidity. There is a need to explore the perceptions about telemedicine among older adults with multimorbidity to tailor it to the needs of older adults with multiple chronic conditions. This study aims to explore the perceptions about telemedicine among older patients with multimorbidity. A qualitative study was conducted using semistructured interviews. The interview questions examined older adults' perspectives about telemedicine, including their expectations regarding telemedicine services and the factors that affect its use. Thematic analysis was performed using NVivo (version 12; Lumivero). The study was reported using the Standards for Reporting Qualitative Research guidelines. In total, 29 patients with multimorbidity-21 (72%) female patients and 8 (28%) male patients with a mean age of 69 (SD 10.39) years-were included. Overall, 4 themes and 7 subthemes emerged: theme 1-perceived benefit of telemedicine among older adults with multimorbidities, theme 2-appropriate use of telemedicine for multimorbid care, theme 3-telemedicine system catering to the needs of older patients, and theme 4-respect patients' decision to decline to use telemedicine. Telemedicine for older adults with multimorbidity should focus on those with stable conditions. This can help increase access to care for those requiring continuous condition monitoring. A structured telemedicine program and patient-centered services can help increase patient acceptance of telemedicine. However, health care providers must accept the limitations of older patients that may prevent them from receiving telemedicine services.

Comparative evaluation of UAV photogrammetry and mobile laser scanner for flexible pavement failure detection in developing countries

Flexible pavements constitute a critical infrastructure that, throughout its life cycle, faces degradation caused by climatic variables and traffic loads. This deterioration affects their mechanical properties, leading to cracks and failures that reduce their functionality and longevity. It is therefore imperative that advanced analytical methodologies are applied to identify the appropriate level of intervention to ensure their optimal serviceability. Recently, technological innovations have emerged that allow the detailed assessment of flexible pavements in an efficient manner, covering large areas in a short time. This research focuses on whether drone photogrammetry (UAV) or mobile laser scanning (MLS) is more appropriate for the diagnosis of surface imperfections in flexible pavements in the context of developing countries, as well as the impacts that its adoption could have on road assessment. The qualitative study is based on a literature review and uses the Choosing by Advantages (CBA) method to evaluate and compare the decisive qualities in the selection of technologies. The results indicate that the mobile laser scanner provides accurate topographic and geometric characterisation at the millimetre level. However, drone photogrammetry, standing out for its high flexibility, low cost and ease of operation, presents itself as the most viable solution for continuous road condition monitoring.

(Invited) Wireless Healthcare Flexible Sensor System

Daily continuous health condition monitoring is now of great interests using multi-functional flexible sensors. In particular, vital signals from skin are the most convenient way to measure continuously data collections during daily life. For this purpose, flexible sensor sheets allow it to detect multiple information from skin precisely due to conformable covering over skin surface. To realize this platform, multiple developments including flexible sensors, wireless system, and edge system to analyse huge size of datasets are required. In this talk, I introduce a wireless multiple sensor sheet with real-time data analyses on a smartphone as an edge system. The system has a function to display real-time analysed results on a smartphone to notify the users about their conditions.Although a lot of researches and developments are still required for practical application, these studies may help to open a door for the next step of flexible electronics.

Система непрерывного мониторинга технического состояния и оперативной диагностики паровой турбины

Power equipment deterioration occurs during operation, it causes loss in reliability and efficiency, unscheduled shutdowns, and accidents. Now predictive analytics is one of the promising directions in the field of power engineering which allows to control and analyze the technical condition of power equipment. The problems of localization of deviation of technological parameters and detection of anomaly in the operation of power equipment are consistently solved in the framework of predictive analytics. The problems of deviations localization and anomalies detection are solved by the methods of statistical modeling and the classification algorithms respectively. However, for steam turbines the localization of deviation and the detection of anomalies having slow-flowing character are a difficult problem. Therefore, the issue of development of a method for continuous monitoring of technical condition and maintenance diagnostics based on a mathematical model of the steam turbine section flow characteristics is worth noticing. The method allows us to consider the effect of changes in the open flow area of the individual sections of a steam turbine on the pressure distribution over the steam flow path. The model of a steam turbine has been developed within the matrix formalization methodology. The solution of the system of linear and nonlinear equations is carried out by methods of computational mathematics. The solution of the optimization problems of the steam flow path diagnostics is carried out by methods of mathematical programming. A mathematical model of the cogeneration steam turbine Т-250/305-23.5-DB and a method for continuous condition monitoring and maintenance diagnostics of steam turbine have been developed. It allows us to localize deviation and detect anomaly by recovery of the open flow area of the individual sections of a steam turbine based on the pressure distribution over the steam flow path. The results of the statistical analysis prove that a mathematical model of the cogeneration steam turbine Т-250/305-23.5-DB has been recognized adequate. The method for continuous condition monitoring and maintenance diagnostics of steam turbine has demonstrated consistency of the obtained results and ability to solve diagnostic problems in practice. The developed model and method can be used as a module in the development of a software package for predictive analytics of power equipment.

Comparison of Smartphone-Based and Drone-Based Approaches for Assessing Road Roughness

Continuous road-pavement condition monitoring is essential to maintain the effectiveness and performance of road infrastructure. It also helps to optimize resource allocation and prioritization for pavement rehabilitation programs. The traditional methods that have been used for assessing road pavement condition are high cost and time consuming. Contrary to conventional methods, low-cost and new emerging technologies such as smartphones and drones can be used. The study aims to investigate the merits of using two different types of device for assessing road roughness: the smartphone as a response-type and the drone as a non-contact-type device. To this end, firstly, linear vertical acceleration data is collected by using a smartphone that is mounted on a car dashboard. Then, the collected data is used for calculating vertical displacement data using a fast Fourier transform. The displacement data is used for calculating international roughness index (IRI) as a measure of road roughness. Secondly, a drone flies over the studied road to construct a 3D model, extract the longitudinal profile, and eventually calculate the accurate IRI values of the road. Finally, the results of the approaches above will be compared with the results of a road surface profiler (RSP) as a reference method. Although the results reveal a significant correlation (above 0.8) of both methods with the RSP method, from an operational point of view, the smartphone was more cost and time effective, and also an easy-to-use approach for the purpose.

Sensing the structural behavior: A perspective on the usefulness of monitoring information for bridge examination

Managing existing civil infrastructure is challenging due to evolving functional requirements, material aging, and climate change. With increasingly limited economic, environmental, and material resources, more sustainable solutions for practical asset management are required. Significant efforts have been made to monitor civil infrastructure, such as bridges.In-situmeasurements are collected with the aim of improving the accuracy of structural capacity evaluations. Monitoring data collected through bridge load testing, continuous condition monitoring, and non-destructive tests provides structural-behavior information that could significantly influence structural-safety examinations. Nonetheless, monitoring techniques are often costly, and the monitoring costs may not always justify the benefits of the information gained. This paper proposes a short perspective of the potential impact of monitoring activities to assess the structural safety of existing bridges. A full-scale bridge in Switzerland is used as an example. Future research needs are also proposed.

Investigation of Crack in Beam Structure using an Adaptive-Genetic Algorithm (AGA)

Fault detection and continuous condition monitoring in structural and machine elements are very sensitive topics and gaining significant value as a current research area. Due to the continuous loading and unloading of these elements, fatigue occurs. For the above-mentioned reason, crack is initiated and propagated. The initiation of any type of crack changes the physical properties of the structural and machine elements, which directly affects the lifetime of the element. The presence of any discontinuity changes the physical properties of the element, which also changes elastic properties. These alterations in physical properties change the modal properties of the structural elements. These changes in the vibration criteria can be used for the identification and quantification of the damage. In this research work, the vibration parameters are combined with Artificial Intelligence (AI) to predict the damage location. Here the natural evolution-based Genetic Algorithm (GA) has been used for the training of vibration features (frequencies). It has been discovered that the original AI methods are sometimes not able to give the proper prediction of damage location as they may be trapped in the local optimum. So, to counteract this loophole and to make it more flexible so that it can adjust to the constraints of real-life problems, a data mining method using Regression Analysis (RA) has been proposed and the results have been compared.

A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators.

Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from changes in the magnetic signature of the synchronous generator, obtained from frequency spectra of monitored signals using induction magnetic field sensors. The process forms a set of time series that reflects constructive and operational characteristics of the machine. The Shewhart control chart method is applied for anomaly detection in these time series, allowing the detection of changes in the machine magnetic signature. This method is employed in an algorithm for continuous condition monitoring of synchronous generators, presenting as output a global change indicator for the multivariable problem associated with magnetic signature monitoring. Correlation matrices are used to improve the algorithm response, filtering series with similar variation patterns associated with detected events. The proposed method is validated through tests on an experimental bench that allows the controlled imposition of faults in a synchronous generator. The proposed global change indicator allows the automatic detection of stator and rotor faults with the machine synchronized with the commercial power grid. The proposed methodology is also applied on data obtained from an equipment installed in a 305 MVA synchronous generator of a hydroelectric power plant where the evolution of an incipient fault, i.e., a mechanical vibration fault, has been detected.

A 3D Printed Compact and Intelligent Power Module Based on Vibration Energy Harvester Integrated with Self‐Powered Triboelectric Sensor for Industry 4.0 IoT Applications

AbstractIn industry 4.0, continuous condition monitoring is essential to keep the system well maintained and stable. However, current monitoring systems demand periodic replacement/recharging of batteries. Herein, a compact vibration‐driven intelligent power module is proposed with excellent energy harvesting and self‐powered vibration sensing capability for industrial 4.0 IoT applications. By implementing newly designed 3D printed flat‐circular Z‐shaped springs, excellent performance is achieved from electromagnetic vibration energy harvester (VEH), whereas the integrated triboelectric‐based self‐powered vibration sensor (SVS) on springs is used for real‐time detection and monitoring of machinery vibration conditions simultaneously. VEH can deliver a maximum peak power of 1.95 W at 16 Hz and demonstrates excellent performance under a wide range (10‐100 Hz), whereas SVS exhibits an excellent vibration sensitivity (2.85 V Hz–1, 3.41 V s2 m–1). The module is structurally and dimensionally like a standard D‐battery consisting of VEH, SVS, power management circuit (PMC), rechargeable Li‐Ion battery, and universal serial bus type‐c port (USB‐C). The module can sufficiently charge a built‐in battery from industry‐standard air‐pump motor and car engine vibrations such that the harvested energy from automobile engine vibration is successfully utilized to power multiple sensors (temperature, humidity, and pressure) and wireless transmission circuitries to realize a self‐powered smart wireless condition monitoring system.

(Invited) Wireless Healthcare Flexible Sensor System with Comfortable and Precise Monitoring

Daily continuous health condition monitoring is now of great interests using multi-functional flexible sensors. In particular, vital signals from skin are the most convenient way to measure continuously data collections during daily life. For this purpose, flexible sensor sheets allow it to detect multiple information from skin precisely due to conformable covering over skin surface. To realize this platform, multiple developments including flexible sensors, wireless system, and potential application are required. In this talk, I introduce a wireless multiple sensor sheet system with multiple printed flexible sensors such as skin perspiration, respiration, electrocardiogram etc. The system makes a feedback alarm if abnormal human condition signals are detected. Furthermore, to monitor the vital signals precisely and comfortably even under activity such as walking and running, device architecture is also discussed.Although a lot of researches and developments are still required for practical application, these studies may help to open a door for the next step of flexible electronics.

Detecting corrugation defects in harbour railway networks using axle-box acceleration data

Sea- and inner ports are intermodal traffic nodes that play an important role in transportation, especially in the transportation of goods. The appearance of track defects in a harbour railway network has a negative impact on safety, cost and comfort (for example due to noise emission). The analysis of data obtained by embedded acceleration sensors, which are installed at the axle box of an equipped in-service vehicle, allows for continuous condition monitoring of the track infrastructure. The German Aerospace Center (DLR) develops prototypical modular multi-sensor systems that are used in different operational environments, including on a shunter locomotive operating in an industrial harbour railway network in Braunschweig, Germany. Within the HavenZuG research project, extensive rail longitudinal profile and track geometry measurements have been performed using established inspection methods to obtain the true underlying condition of the railway network. In the present paper, methods for gaining relevant information from the axle-box acceleration (ABA) data are presented and validated with the given reference data. The focus is on detecting defects that are visible in the rail longitudinal profile, mainly rail corrugation. It can be shown that ABA data gathered during everyday shunting operation can be used for detecting corrugation and for inferring rail longitudinal profile parameters.

Valuation of Continuous Monitoring Systems for Engineering System Design in Recurrent Maintenance Decision Scenarios

Abstract Condition monitoring plays a crucial role in improving system failure resilience, preventing tragic consequences brought by unexpected system failure events, and saving the consequential high operation and maintenance costs. Continuous condition monitoring systems have been applied to diversified engineering systems for well-informed operational decision-makings. Although research has been devoted to predicting system states using the continuous data flow, there still lacks a systematic decision-making framework for system designers to assess the value of such monitoring systems at the design stage therefore making system design decisions on adopting monitoring systems to maximize the benefits. This paper constructs such a decision-making framework based on the value of information, with which system designers can evaluate expected operation cost reductions under specific operation modes considering the effectiveness of continuous monitoring systems in predicting system failures. Two case studies on a battery energy storage system and a mechanical system, respectively, are employed to illustrate the value evaluation of the monitoring information and the system maintenance process with the aid of different prognostic results based on the monitoring data. Case study results show that the value of monitoring systems will be influenced by the deviation among the equipment group, the accuracy of system-state prediction, and different types of costs involved in the operating process. The adjustment of maintenance actions based on monitoring and prognosis information will help improve the value of monitoring systems.