Ajith Kumar Parlikad

Networked RFID systems in product recovery management

Product recovery involves collection, sorting and reprocessing of returned products to recover value from them. The management of product recovery is characterised by a high level of uncertainty in product returns flow due to the lack of information associated with such products. A case study exercise carried out at various electronics product recovery industries in Europe supports this widely accepted fact. The recent emergence of networked RFID (radio frequency identification) systems is a means of connecting a product tagged with an RFID chip to a network and thereby carrying complete information associated with it throughout its lifecycle. This paper examines the benefits of information provided by such systems in decision-making during product recovery stages and consequently, in product recovery management as a whole.

Information management in the product lifecycle - the role of networked RFID

Radio frequency identification (RFID) is receiving significant attention at present through its low cost deployment in the retail supply chain. This paper introduces the concept of networked RFID developed by the Auto ID Center, and examines its role in supporting more effective product lifecycle management (PLM). In particular, the product tagging approach initiated by the Auto ID Center has centered on ability to access complete and accurate information about a product in a timely manner and is demonstrated to be important for decision making at different stages along its lifecycle

Condition-based maintenance of multi-component systems with degradation state-rate interactions

This paper presents an approach to optimise condition-based maintenance (CBM) of multi-component systems where the state of certain components could affect the rate of degradation of other components, i.e., state-rate degradation interactions. We present a real example of an industrial cold box in a petrochemical plant, where data collected on fouling of its tubes show that the extent of fouling of one tube affects the rate of fouling of other tubes due to overloading. A regression model is used to characterise the state-rate degradation interactions for this example. Further, we optimise the condition-based maintenance policy for this system using simulated annealing. The outcomes of the case study demonstrate that modelling degradation interactions between components in the system can have significant positive impact on CBM policy of the system. The paper therefore tackles a problem that has not been addressed in the literature, paving way for further developments in this important area of research with practical applications.

Quantifying the impact of AIDC technologies for vehicle component recovery

Recovering value from end-of-life vehicles (ELV) has become increasingly important in recent years due to legislative pressures. In this context, cannibalisation of valuable components for possible reuse in secondary markets is becoming a popular option. However, recovery processes are characterised by high levels of uncertainty regarding the quality of components returned at their end-of-life. Hence, the key to making component recovery an cost-effective option is to provide more information regarding the condition of components in the ELV to the recoverer. Emerging automated identification and data capture (AIDC) technologies such as RFID tags and sensor networks can have significant impact on the effectiveness with which product information is generated and shared among the various actors in the product lifecycle. This paper illustrates how decision-making during product recovery can be improved by collecting critical usage data during a vehicles lifecycle and making it readily available to key decision-makers. In particular, we use a probabilistic model to quantify the benefits of automated information capture technologies in improving product recovery processes.

Maintenance Optimization for Asset Systems With Dependent Performance Degradation

This paper presents a model for optimizing maintenance plans for an industrial system consisting of a number of assets with degradation and performance interaction between them. In particular, we consider an asset system with M identical non-critical machines feeding their output to a critical machine. A common repair team performs maintenance on all the machines. All the machines deteriorate over time stochastically independently. In addition to the stochastically independent degradation, the degradation of the non-critical machines also affects the performance of the critical machine. We develop a mathematical model to represent these interactions and performance of the asset system. We also provide a simulation-based numerical solution to optimize the maintenance plan for the system outlining the maintenance intervals for each of the machines.

Data quality assessment: The Hybrid Approach

Various techniques have been proposed to enable organisations to assess the current quality level of their data. Unfortunately, organisations have many different requirements related to data quality (DQ) assessment. For example, some organisations may need to focus on ensuring regulations are met rather than reducing costs. Due to this, organisations may be forced to follow an assessment technique, which may not wholly fit their needs and current situation. Therefore, we propose and evaluate the Hybrid Approach to assessing DQ, which demonstrates how to dynamically configure an assessment technique as needed while leveraging the best practices from existing assessment techniques.

Asset information management: research challenges

In order for engineering organisations to maximise revenue they have to utilise assets in an effective and efficient way. The success of an enterprise often depends on its ability to utilise assets efficiently. This paper explores challenges in managing engineering assets throughout their lifecycle. We examine the role of asset information management in improving the effectiveness with which complex engineering assets are utilised. Furthermore, the importance of quantifying the value of information in asset management is underlined, specifically discussing the issue of information quality in asset management.

Value of condition monitoring in infrastructure maintenance

As many civil infrastructure assets age over time and often exceed their design life, there is an increasing need to assess and monitor their condition so as to allow improved and effective maintenance decisions. In this regard, there is growing interest in implementing advanced condition monitoring techniques to capture the condition of the infrastructure assets. However, there is a lack of understanding in determining the benefits offered by the various condition monitoring techniques, such that the most appropriate system is implemented to reap the benefits. In this context, this paper proposes a methodology to assess the benefits offered by condition monitoring systems and the factors that affect the value delivered by such systems. The proposed methodology is based on Partially Observable Markov Decision Process (POMDP) and is illustrated with an industrial case example.

A Condition-Based Maintenance Model for Assets With Accelerated Deterioration Due to Fault Propagation

Complex industrial assets such as power transformers are subject to accelerated deterioration when one of its constituent component malfunctions, affecting the condition of other components, which is a phenomenon called fault propagation. In this paper, we present a novel approach for optimizing condition-based maintenance policies for such assets by modelling their deterioration as a multiple dependent deterioration path process. The aim of the policy is to replace the malfunctioned component and mitigate accelerated deterioration at minimal impact to the business. The maintenance model provides guidance on determining inspection and maintenance strategies to optimize asset availability and operational cost.

Semi-Markov Decision Process With Partial Information for Maintenance Decisions

A critical factor that prevents optimal scheduling of maintenance interventions is the uncertainty regarding the current condition of the asset under consideration, as well as the rate at which deterioration takes place. However, current maintenance modeling and optimization techniques assume that the condition of the asset is either known, or assumed to have an exponential deterioration rate. In this paper, we present a novel approach to maintenance modeling that removes such assumptions. Here, we employ a Partially Observable Semi-Markov Decision Process (POSMDP) for optimizing maintenance decisions, where the condition of the asset is not fully observable, and decision epochs occur at times following any other type of distribution. This method enables a more realistic way of modeling asset deterioration and optimizing maintenance schedules.