Vehicle Health Monitoring System Research Articles

Deep learning-based vehicular engine health monitoring system utilising a hybrid convolutional neural network/bidirectional gated recurrent unit

Vehicles play a pivotal role in the current era of Industry 4.0 by providing passengers with excellent mobility, comfort, and safety while strengthening national and international economies. Unanticipated vehicular engine issues can hinder performance and lead to costly maintenance. As analytics processes become faster, more accurate, and more reliable, intelligent fault prediction and diagnosis for vehicles, particularly engines, is becoming increasingly popular. To date, hybrid deep learning approaches to vehicle engine diagnostics have been limited, and none have used engine health monitoring and categorisation based on vulnerability assessment and vehicle structural information. This paper introduces a hybrid deep learning-based vehicular engine health monitoring system (VEHMS) decision model using Deep CNN (convolutional neural network)-BiGRU (bi-directional gated recurrent unit). This model monitors a vehicle’s engine health in real-time and classifies its status as good, critical, moderate, or minor condition. Several advanced and hybrid deep learning algorithms were applied to monitor engine health and categorise its status by integrating sensor data with evaluated vulnerability information from an infrastructure vulnerability assessment model. The Deep CNN-BiGRU-based VEHMS decision model outperformed other techniques with an accuracy of 0.8897, ensuring minimal decision losses while classifying engine conditions. This study aims to contribute to developing comprehensive vehicle health monitoring systems and advance the automotive industry by incorporating more intelligent features. The proposed approach can enhance vehicle performance, reliability, and efficiency in the transportation sector by improving engine health monitoring.

Intra-vehicular Communication Protocol for IoT Enabled Vehicle Health Monitoring System: challenges, issues and Solutions

Intra-vehicular Communication Protocol for IoT Enabled Vehicle Health Monitoring System: challenges, issues and Solutions

Vehicle health monitoring and accident avoidance system based on IoT model

The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems.

Integrated Vehicle Health Monitoring on a Truck

There have been few practical of integrated vehicle health monitoring systems for ground vehicles. Once of the challenges of this application is the high dynamics of the engine/transmission and the non-fixed ratio of the transmission, and the drivetrain in general. Further, because of the drivetrain differential, there is no fixed ratio from the input of the differential to the drive wheels. The paper covers the development of a vibration-based vehicle health monitoring system for a truck, and some results of a 1000-mile test run on the system.

A Real Time Implementation of an IOT based Vehicle Health Monitoring System

This paper approaches an IoT based vehicle health monitoring system that is embedded for detecting the condition of a vehicle by monitoring the internal parameters such as heating rate, engine oil level and status of the CO of the vehicle. It is a real time vehicle health monitoring system is designed and developed to detect and identify the actuator and sensor faults created by automatically or manually by the user of the vehicle. Actually, Vehicles need repair after a certain interval of time and if are not repaired at fixed intervals, it can lead to loss of life of the persons travelling on it and there are many key issues which can affect the vehicle. So, the primary objective of this system is developing an IoT based embedded system that can detect the internal condition of a vehicle by evaluating the various parameters that are used to examine in the vehicle’s current health condition. In fact, this is a real time evaluation system that can be used for rapid condition screening. As a result, it provides all reliable information about the vehicle conditions. This IoT based system claims that it can detect and identify actuator and sensor faults with almost minimal detection latency even after lots of disturbances and uncertainties.

Security Enhanced Blockchain based Unmanned Aerial Vehicle Health Monitoring System

In this research work and unmanned aerial vehicle (UAV) that uses blockchain methodology to collect health data from the users and saves it on a server nearby is introduced. In this paper the UAV communicates with the body sensor hives (BSH) through a low-power secure manner. This process is established using a token with which the UAV establishes relationship with the BSH. The UAV decrypts the retrieved HD with the help of of the shared key, creating a two-phase authentication mechanism. When verified, the HT is transmitted to a server nearby in a safe manner using blockchain. The proposed healthcare methodology is analysed to determine its feasibility. Simulation and implementation is executed and a performance of the work is observed. Analysis indicates that the proposed work provides good assistance in a secure environment.

A Review on Vehicle Health Monitoring System

Vehicles need repair after a certain interval of time and if are not repaired at fixed intervals it can lead to accidents. The objective of the project is to continuously notify the owner about the air pressure of tire, quality of exhaust gas, wear of breaks, leakage of oil, the excessive heating of the engine or the blockage in fuel pipe, etc. Tire pressure monitoring sensor is used to get live tire pressure reading, gas sensor gives the readings of amount of carbon dioxide (CO2), Nitrogen Oxides (NOx) etc. as amount and quality of exhaust gives the proper working condition of engine, flow sensor is used that senses the over flow of oil or leakage of oil, temperature sensor is employed in the engine that warns the owner about overheating of engine and a flow sensor is used that senses the proper flow of fuel in the pipe. All these sensor data is displayed on LCD display unit integrated with Arduino microcontroller.

Vehicle Health Monitoring System Using CAN

Modern vehicles are integrated with more number of ECUs and sensors which make them smarter in terms of engine decisions, performance, fuel efficiency, security and stability. ECUs also provide useful data in the form of diagnostic codes via OBDII (on-board diagnostics) protocol, these codes determine a specific issue in the vehicular system which is used by service technicians to address the issues occurred. This paper deals with the exploring the potential of OBDII codes from engine ECU and compute the data on an Android device to monitor the vehicle health status in real-time. The whole idea is to provide portable diagnosis solutions and integration of such system into in-vehicle entertainment like Google’s Android Auto and Apple’s CarPlay which can be one of key component for V2X and IOVs (Internet of Vehicles).

Vehicle Health Monitoring System Using ARDUINO and IOT

The paper portrays the execution of a model framework utilized for effective constant information securing, motor warming and blockage of fuel pipe. Legitimate vehicle observing and support can spare time, cash and enhance the possession encounter. Our framework takes a shot at Arduino and IoT stage used to separate different parameters like motor warming and blockage in fuel pipe and so forth for protected and cautious driving. The information is sent to IoT which can be checked both by vehicle producer through distributed computing and proprietor of the vehicle by the android application. The equipment unit comprises of Arduino, WI-FI module, android based portable and distinctive parameter checking sensors module.

Conceptual Study on Integration of Engine Health Monitoring (EHM) System with Integrated Vehicle Health Monitoring (IVHM) System

AbstractAn approach to upgrade existing control system and lean architecture principle is presented to optimize the system along with reduction of weight by understanding the physical system design, Engine Health Monitoring (EHM) system and data study from Integrated Vehicle Health Monitoring (IVHM) system (Aaseng n.d.). This paper brings out the integration of EHM sufficiency with IVHM system. Research effort to describe the hardware, software, algorithms and engine parameters required to monitor various events capturing, health and life management issues with IVHM. Here basic functionality will be performance monitoring, vibration monitoring, event monitoring (including stall, surge, over speed, flameout, over temperature, oil parameter and life monitoring) anti ice system with IVHM requirement of mission completion, safety and data protection.A systematic SE approach and methodology with need and requirement is generated using DOORS software (INCOSE 2011). Full requirement capture with engine designer and aircraft designer (Harinarayana, Developing light combat aircraft: foresight as the guiding principle 2004) for integrating with IVHM (Philip A. Scandura October 30, 2005)has been studied and brought out to model the concept using Rhapsody software. Here various operational scenarios has been attempted to make the functional model. Functional model has been tested for various operational issues and a final configuration has been proposed to the designer (Jaw, Van and Mink 7 – 10 May 2007). This study has brought out a perspective work model for a designer and as a result of this the final product will be tested for integrating the EHM with IVHM

Emerging Needs for Pervasive Passive Wireless Sensor Networks on Aerospace Vehicles

NASA is investigating passive wireless sensor technology to reduce instrumentation mass and volume in ground testing, air flight, and space exploration applications. Vehicle health monitoring systems (VHMS) are desired on all aerospace programs to ensure the safety of the crew and the vehicles. Pervasive passive wireless sensor networks facilitate VHMS on aerospace vehicles. Future wireless sensor networks on board aerospace vehicles will be heterogeneous and will require active and passive network systems. Since much has been published on active wireless sensor networks, this work will focus on the need for passive wireless sensor networks on aerospace vehicles. Several passive wireless technologies such as MEMS, SAW, backscatter, and chipless RFID techniques, have all shown potential to meet the pervasive sensing needs for aerospace VHMS applications. A SAW VHMS application will be presented. In addition, application areas including ground testing, hypersonic aircraft and spacecraft will be explored along with some of the harsh environments found in aerospace applications.

Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications

Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols.

Design manufacturing integration and flight testing of a health monitoring system for a prototype unmanned airborne vehicle

This article describes the design, development, build and flight testing of a health monitoring system for the landing gear and the electrical power system on board the Demon prototype unmanned airborne vehicle. Demon is a flying technology demonstrator which successfully flew in September 2010. The Demon can achieve pitch and roll control without the use of hinged control surfaces, by instead using fluidic devices based on the Coanda effect, attaining low-maintenance, high-manoeuvrability operations. A vehicle health monitoring system was added on board between the first and the second flight test campaigns. The integration of the health monitoring system into the vehicle is discussed as a whole. The key health monitoring sub-systems include data logging and real-time measurement of several parameters. This includes systems to measure Voltage and current from the main batteries, landing gear stress, suspension travel, wheel hub acceleration and shock absorber pressure. Wherever possible, the use of commercially available components was maximised to minimise development time and cost. Some example results of system health monitoring during flight trials are presented.

Advances in Integrated Vehicle Health Monitoring Systems

One of the fastest growing fields of study in defense market currently is Integrated Vehicle Health Monitoring (IVHM). These systems perform collection and analysis of data concerning operating parameters and damage information of vehicles in real-time and periodically send them to the base station for appropriate action. Recent advances in materials and sensor networks and technologies promise development of such systems for land, water, and aerial vehicles. These IVHM systems are of immense use in defense services which require their vehicles and systems to operate normally even under hostile and harsh environments. This work will discuss various issues related to IVHMs and prominent sensor technologies available to build such systems. It will also include the structure of a general purpose IVHM of a vehicle which can be used to monitor its inner operating parameters and damage information of its components.

A vehicle health monitoring system evaluated experimentally on a passenger vehicle

A vehicle health monitoring system based on analytical redundancy, is developed for automated passenger vehicles. A residual generator and a residual processor are designed together to detect and identify actuator and sensor faults of the Buick LeSabre rapidly. The residual generator includes fault detection filters and parity equations. It uses the control commands and sensor measurements to generate the residuals, which have a unique static pattern in response to each fault. Then, the residual processor interrogates the residuals by matching them to one of several known patterns. It computes the probability of each hypothesis conditioned on the history of residuals. The fault detection latency is reduced by integrating the design of the residual generator and the residual processor. The vehicle health monitoring system is evaluated in real-time on a Buick LeSabre. The vehicle sensor and actuator faults are simulated artificially by the computer or created manually by the driver. In one experiment, a real intermittent sensor fault occurred and was immediately detected and identified. The real-time evaluation demonstrates that the vehicle health monitoring system can detect and identify actuator and sensor faults under various disturbances and uncertainties with almost minimal detection latency

Autonomous Component Health Management with Failed Component Detection, Identification, and Avoidance

This article describes a novel scheme for autonomous component health management (ACHM) with failed actuator detection and identification and failed sensor detection, identification, and avoidance. This new scheme has features that are very superior to those with triple redundant sensing and voting, yet requires fewer sensors; it can be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided in this article. Simulation results for its application to a single-axis spacecraft attitude control system with a third-order plant and dual-redundant measurements of the system states are presented. The ACHM scheme fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is autonomous; is adaptive; works in real time; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures, reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open- and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary estimates of the system states, detects and identifies failed sensors and actuators, and avoids failed sensors in generating preliminary estimates of the system states with a fixed-gain Kalman filter that provides model-based state estimates, which are utilized in the failure detection logic, and generates optimal estimates for the system states. The simulation results show that ACHM can successfully detect, identify, and avoid sensor failures that are single or multiple; persistent and intermittent; and hard-over, soft, and zero-output types. It is now ready to be tested on a computer model of an actual system.