On-board Health Monitoring System Research Articles

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.

An Adaptive Fault Diagnosis of Electric Vehicles: An Artificial Intelligence Blended Signal Processing Methodology

This article demonstrates an innovative design of a sensorless technique to diagnose, monitor, and broadcast faults in an electric vehicle’s (EV) propulsion operating conditions. By utilizing the artificial intelligence with a signal processing mixed clustering technique, an onboard health monitoring system (HMS) has been presented. The clustering technique uses a data-mining approach to prevent future failures for predictive maintenance planning, which is novel. For example, the propulsion inverter is equipped with a diagnostic system that uses the proposed algorithm to compare the reference gate-driving signal with the actual output voltage of the voltage source inverter (VSI). This article presents different failure scenarios of the inverter and demonstrates the capability to be applied to other components, such as brakes and motors. To validate the proposed technique, the necessary algorithm calculations, simulation, and laboratory prototype results are provided. The proposed work is proven accurate with fast response in healthy and faulty conditions.

Developing an innovative health monitoring device to improve communication between passengers and cabin attendants during Inflight Emergencies

Introduction Medical emergencies occurring onboard commercial flights are exceedingly rare, however if it happens, they put cabin-, flight crew and fellow passengers under enormous stress levels. The use of a Health Monitoring System could aid the cabin crew in early detecting critical health conditions. Additionally, for passengers there is no possibility to discreetly inform the crew about emergencies since there is only a Passenger Call Button available. Research Question This paper analyses passengers´ user acceptance of the Health Monitoring System as well as the Emergency Button. Method Participants needed to complete an online survey and were presented prototypes of the Health Monitoring System as well as the Emergency Button. User acceptance was measured using the Van der Laan scale. A total of 41 participants contributed to the study. Result The user ratings for the Onboard Health Monitoring System were low mainly due to privacy concerns of the passengers. Since only one use case for the Emergency Button has been presented to the participants of the study, the user acceptance ratings were also low. Discussion Passengers were reluctant to share sensitive data. However, the majority (over 70%, n≥ 11, effective sample size=16)) agreed to have the most important vital signs such as heart rate, blood pressure, body temperature, oxygen saturation measured. Conclusion To increase user acceptance of the Health Monitoring System it is important for the passengers to ensure that sensitive data is handled and stored carefully. Additional detailed usability assessments would be necessary for both devices to draw further conclusions about system design.

Bolster spring fault detection strategy for heavy haul wagons

ABSTRACTAn on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40 t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events.

Prognostic System for Power Modules in Converter Systems Using Structure Function

This paper proposes an on-board methodology for monitoring the health of power converter modules in drive systems, using vector control heating and structure function to check for degradation. It puts forward a system that is used on-board to measure the cooling curve and derive the structure function during idle times for maintenance purposes. The structure function is a good tool for tracking the magnitude and location of degradation in power modules. The ability to keep regular track of the actual degradation level of the modules enables the adoption of preventive maintenance, reducing or even eliminating altogether the appearance of failures during operation, significantly improving the availability of the power devices. The novelty in this work is the complete system that is used to achieve degradation monitoring, combining the heating technique and the measurement without additional power components except the measurement circuit which can be integrated into the gate drive board and the challenges encountered. Experimental results obtained from this show that it is possible to implement an on-board health monitoring system in converters which measures the degradation on power modules.

An overview: modern techniques for railway vehicle on-board health monitoring systems

ABSTRACTHealth monitoring systems with low-cost sensor networks and smart algorithms are always needed in both passenger trains and heavy haul trains due to the increasing need for reliability and safety in the railway industry. This paper focuses on an overview of existing approaches applied for railway vehicle on-board health monitoring systems. The approaches applied in the data measurement systems and the data analysis systems in railway on-board health monitoring systems are presented in this paper, including methodologies, theories and applications. The pros and cons of the various approaches are analysed to determine appropriate benchmarks for an effective and efficient railway vehicle on-board health monitoring system. According to this review, inertial sensors are the most popular due to their advantages of low cost, robustness and low power consumption. Linearisation methods are required for the model-based methods which would inevitably introduce error to the estimation results, and it is time-consuming to include all possible conditions in the pre-built database required for signal-based methods. Based on this review, future development trends in the design of new low-cost health monitoring systems for railway vehicles are discussed.

A unified approach to detecting and trending changes caused by mechanical faults in rotating machinery

For detecting faults in rotating machinery, different fault types generally require different techniques for the effective detection of the faults; hence, many different techniques have been developed. However, we often find that the existing detection techniques are either incapable or ineffective for many new fault types. Therefore, we will need to develop brand new methods after the fault event. This can significantly constrain the usefulness and effectiveness of machine health monitoring systems. In this article, we propose a unified signal processing approach to detecting and trending changes caused by various types of faults in rotating machinery. The theoretical foundation of the proposed technique is based on extracting the differences between the synchronously averaged (angularly resampled) signals acquired under the changing health conditions after aligning their phases. The aligning of the phases is achieved through a novel and efficient approach, which is performed in the frequency domain. The alignment enables a direct comparison between the amplitudes of the synchronously averaged signals and provides a trustworthy evaluation using the same reference datum. The proposed method is novel and conceptually unsophisticated, and its effectiveness is demonstrated using vibration data from rotating machines with several different types of faults. The results have shown that this single unified change detection approach can be very effective in detecting and trending changes caused by many different types of faults in rotating machines. This is an ideal technique to be implemented for testing on aircraft engine onboard health monitoring systems.

Real time crack detection using mountable comparative vacuum monitoring sensors

Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.