Integrated Vehicle Health Monitoring Research Articles

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.

Operational Availability Optimization Model Based on the Integration of Predictive and Scheduled Maintenance

Health monitoring technologies and data analytics are increasingly widespread in the aviation industry following the growth in the capacity and speed of abundant and accurate data generation and transmission from the aircraft systems. These advances are fueling a change process in aircraft maintenance strategy towards a more proactive, precise, and effective approach consolidated in the concepts of Integrated Vehicle Health Monitoring (IVHM) and Prognostics and Health Management (PHM). Following that, several model-based and data-driven prognostics methods for Remaining Useful Life (RUL) estimation have been developed in the pursuit of improving predictive maintenance interventions for different types of components. Recent papers showcased the significant challenges faced to achieve forecast accuracy as posed by the inherent uncertainty involved in the functional dynamics of complex systems. This work acknowledges these difficulties and tackles variability by embracing it in the methodology deployed by means of considering in its framework the confidence intervals associated with the estimates for a predefined level of confidence. 
 Nevertheless, the ability to pinpoint times-to-failure by itself is arguably not enough to yield better operational results and improve support levels of service given that scattered standalone interventions may even cause failure occurrences and total downtime to increase. This study demonstrates the rationale behind those effects and exposes the necessity for a method for achieving a compromise to optimally accommodate the concurrent economic, reliability and maintainability goals which are, respectively, the maximization of component useful life expenditure, the minimization of the running-into-failure risk and the minimization of total downtime. 
 Further on, the article explores the problem in detail identifying the key parameters pointed out in the literature that need to be addressed by the modelling process to ensure the soundness of the method. The text then proposes a solution consisting of an innovative analytical model that optimizes operational availability through the dynamic allocation of flight-hours to each aircraft part of a fleet based on the integration of predictive and scheduled maintenance, minimizing total downtime, while accounting for prognostics uncertainty and the associated the risk of failure and incurring in corrective maintenance. The intended outcome is the capability of providing dynamic maintenance plans specially adjusted to each tail number according to its assessed health status and a calculated prognostic that considers predetermined future flights specifically attributed to optimize the overall availability of the fleet.
 An illustrative case study involving multiple components with different aging parameters equipping the aircraft of a small military fleet operating from a single base was used to test the solution and produced results that corroborate the validity of the approach adopted and demonstrate the model’s value and effectiveness. The results also indicate there is significant potential to expand the study and encourage its further development to contemplate multiple-base scenarios and incorporate more detailed aspects such as tasks location within the aircraft, availability of spare parts and resources in general, out-of-phase items and its implementation together with a simulation tool to generalize its application.
 The main contributions of the study are twofold. It adds on the theoretical complexity by tackling systems of systems instead of the predominant single component approach, and it provides a model with an optimizing objective function to improve maintenance planning in real-life.

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

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.

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.