Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles
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paper_title: The analytic hierarchy process applied to maintenance strategy selection

paper_content:

Abstract This paper describes an application of the Analytic Hierarchy Process (AHP) for selecting the best maintenance strategy for an important Italian oil refinery (an Integrated Gasification and Combined Cycle plant). Five possible alternatives are considered: preventive, predictive, condition-based, corrective and opportunistic maintenance. The best maintenance policy must be selected for each facility of the plant (about 200 in total). The machines are clustered in three homogeneous groups after a criticality analysis based on internal procedures of the oil refinery. With AHP technique, several aspects, which characterise each of the above-mentioned maintenance strategies, are arranged in a hierarchic structure and evaluated using only a series of pairwise judgements. To improve the effectiveness of the methodology AHP is coupled with a sensitivity analysis.
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paper_title: PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment

paper_content:

The production system and its maintenance system must be now developed on "system thinking" paradigm in order to guarantee that Key Performance Indicators (KPI) will be optimized all along the production system (operation) life. In a recursive way, maintenance system engineering has to integrate also KPI considerations with regards to its own enabling systems. Thus this paper develops a system-based methodology wherein a set of KPIs is computed in order to verify if the objectives of the production and maintenance systems are satisfied. In order to help the decision-making process for maintenance managers, a "unified" generic model have been developed. This model integrates (a) the interactions of the maintenance system with its enabling systems, (b) the impact of the maintenance strategies through the computation of some key performance indicators, and (c) different kinds of knowledge regarding the maintenance system and the system of interest, including quantitative and qualitative knowledge. This methodology is based on an executable unified model built with Probabilistic Relational Model (PRM). PRM allows a modular representation and inferences computation of large size models. The methodology added-value is shown on a test-bench.
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paper_title: A review on machinery diagnostics and prognostics implementing condition-based maintenance

paper_content:

Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.
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paper_title: Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

paper_content:

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines' operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.
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paper_title: Maintenance management: literature review and directions

paper_content:

Purpose – The purpose of this paper is to review the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitioners.Design/methodology/approach – The paper systematically categorizes the published literature and then analyzes and reviews it methodically.Findings – The paper finds that important issues in maintenance management range from various optimization models, maintenance techniques, scheduling, and information systems etc. Within each category, gaps have been identified. A new shift in maintenance paradigm is also highlighted.Practical implications – Literature on classification of maintenance management has so far been very limited. This paper reviews a large number of papers in this field and suggests a classification in to various areas and sub areas. Subsequently, various emerging trends in the field of maintenance management are identified to help researchers specifying gaps in the literature and direct research efforts suitably.Originali...
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paper_title: Introduction: The Basics of Failure

paper_content:

In this chapter, the definition of a failure is provided and the difference between a failure mode and failure mechanism is explained. Then, the concepts of load and load-carrying capacity are introduced and their mutual relation is discussed. Moreover, the difference between internal and external loads is treated and the large variety of different load types and failure mechanisms is illustrated.
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paper_title: A Framework of an Intelligent Maintenance Prognosis Tool

paper_content:

The technology of prognosis has become a significant approach but its implementation in maintenance has a major extension. The ability prognosis in the medical area has been established to estimate the future of human health. However, in maintenance, application of prognosis is not yet seen as a practical use for making better maintenance decision. To date, research in this area has been done in proposing prognosis techniques or model but leaving the implementation of prognosis as their future work. In this paper, an overview of prognosis in maintenance is presented. By using the data-driven approach, a framework for implementing of an intelligent maintenance prognosis tool is introduced. The framework utilizes the existing equipment operating performance data in the industry for prognosis process. Next, the framework combines the ability of prognosis in estimating remaining useful life (RUL) of equipment with the maintenance action knowledge to generate a well-received maintenance plan.
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paper_title: Consensus self-organized models for fault detection (COSMO)

paper_content:

Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles' data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.
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paper_title: Connected Vehicle Diagnostics and Prognostics, Concept, and Initial Practice

paper_content:

We propose a new concept of Connected Vehicle Diagnostics and Prognostics (CVDP) to address some of the challenges in vehicle system fault diagnostics and prognosis, such as the diagnostics of unexpected new faults, and infrequent or intermittent faults. As an initial practice, this concept has been implemented in the vehicle design validation process at GM. This paper presents the implementation details, and some case studies.
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paper_title: Predictive algorithm to determine the suitable time to change automotive engine oil

paper_content:

Recently, emerging technologies related to various sensors, product identification, and wireless communication give us new opportunities for improving the efficiency of automotive maintenance operations, in particular, implementing predictive maintenance. The key point of predictive maintenance is to develop an algorithm that can analyze degradation status of automotive and make predictive maintenance decisions. In this study, as a basis for implementing the predictive maintenance of automotive engine oil, we propose an algorithm to determine the suitable change time of automotive engine oil by analyzing its degradation status with mission profile data. For this, we use several statistical methods such as factor analysis, discriminant and classification analysis, and regression analysis. We identify main factors of mission profile and engine oil quality with factor analysis. Subsequently, with regression analysis, we specify relations between main factors considering the types of mission profile of automotive: urban-mode and highway-mode. Based on them, we determine the proper change time of engine oil through discriminant and classification analysis. To evaluate the proposed approach, we carry out a case study and have discussion about limitations of our approach.
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paper_title: Predictive maintenance with multi-target classification models

paper_content:

Unexpected failures occurring in new cars during the warranty period increase the warranty costs of car manufacturers along with harming their brand reputation. A predictive maintenance strategy can reduce the amount of such costly incidents by suggesting the driver to schedule a visit to the dealer once the failure probability within certain time period exceeds a pre-defined threshold. The condition of each subsystem in a car can be monitored onboard vehicle telematics systems, which become increasingly available in modern cars. In this paper, we apply a multi-target probability estimation algorithm (M-IFN) to an integrated database of sensor measurements and warranty claims with the purpose of predicting the probability and the timing of a failure in a given subsystem. The multi-target algorithm performance is compared to a single-target probability estimation algorithm (IFN) and reliability modeling based on Weibull analysis.
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paper_title: A predictive algorithm for estimating the quality of vehicle engine oil

paper_content:

Recently, with emerging technologies, visibility of vehicle information over the whole lifecycle becomes possible. The visibility opens up new challenging issues for improving the efficiency of vehicle operations. One of the most challenging problems arising during the middle of life (MOL) of vehicles is the predictive maintenance on engine oil. For this, in this study, we focus on developing a predictive algorithm to estimate the quality of the engine oil of a vehicle by analyzing its degradation status with mission profile data. For this purpose, we specify the relations between indicators of engine mission profiles and oil quality indicators using principal component analysis and regression method. Then, we develop a heuristic algorithm for estimating the value of a quality indicator of engine oil based on them. To evaluate the proposed approach, we carry out a case study and computational experiments. Significance: This study deals with a prognostic maintenance approach for vehicle engine oil, which recently has been highlighted as a cost-effective method using product status data provided by emerging technologies.
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paper_title: Condition Based Maintenance for light trucks

paper_content:

Army ground vehicles often operate in extremely severe environmental and battlefield conditions. There are challenges for the reliability of the military ground vehicle fleet, which need to be addressed. Condition Based Maintenance (CBM) allows maintenance to be performed based on evidence of need provided by reliability modeling and/or other enabling technologies, thus reducing maintenance costs and increasing vehicle availability. The architecture of the Intelligent Vehicle Health Management System (IVHMS) for light trucks is presented. A fuzzy model is developed to diagnose the axle fatigue of the vehicle. The extraction of the fuzzy rules is based upon expert knowledge and a linear damage model. Training data will be used to modify the membership functions and the fuzzy If-Then rules to improve the quality of the fuzzy model for fault diagnostics. The improvement of the fuzzy model will be carried out using re-clustering operation and membership function optimization.
---
paper_title: Consensus self-organized models for fault detection (COSMO)

paper_content:

Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles' data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.
---
paper_title: A review on machinery diagnostics and prognostics implementing condition-based maintenance

paper_content:

Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.
---
paper_title: Connected Vehicle Diagnostics and Prognostics, Concept, and Initial Practice

paper_content:

We propose a new concept of Connected Vehicle Diagnostics and Prognostics (CVDP) to address some of the challenges in vehicle system fault diagnostics and prognosis, such as the diagnostics of unexpected new faults, and infrequent or intermittent faults. As an initial practice, this concept has been implemented in the vehicle design validation process at GM. This paper presents the implementation details, and some case studies.
---
paper_title: A Framework of an Intelligent Maintenance Prognosis Tool

paper_content:

The technology of prognosis has become a significant approach but its implementation in maintenance has a major extension. The ability prognosis in the medical area has been established to estimate the future of human health. However, in maintenance, application of prognosis is not yet seen as a practical use for making better maintenance decision. To date, research in this area has been done in proposing prognosis techniques or model but leaving the implementation of prognosis as their future work. In this paper, an overview of prognosis in maintenance is presented. By using the data-driven approach, a framework for implementing of an intelligent maintenance prognosis tool is introduced. The framework utilizes the existing equipment operating performance data in the industry for prognosis process. Next, the framework combines the ability of prognosis in estimating remaining useful life (RUL) of equipment with the maintenance action knowledge to generate a well-received maintenance plan.
---

Title: Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles
Section 1: Introduction
Description 1: Introduce the concept of telematics in commercial vehicles and explain its components and benefits, focusing on how it improves efficiency, reduces costs, and enhances asset management.

Section 2: An overview of PHM system for vehicle
Description 2: Discuss the literature on prognostics systems in commercial vehicles, highlighting specific models and methods developed for predicting component failures and improving maintenance strategies.

Section 3: Challenges
Description 3: Identify and elaborate on the main challenges facing the development and deployment of PHM systems in commercial vehicles, including technical, economic, and practical obstacles.

Section 4: A look to the future
Description 4: Explore future trends and potential advancements in telematics-based PHM systems, discussing how improved sensors, integrated data analysis, and predictive modeling could further enhance vehicle maintenance and fleet management.