Rosmaini Ahmad

An overview of time-based and condition-based maintenance in industrial application

This paper presents an overview of two maintenance techniques widely discussed in the literature: time-based maintenance (TBM) and condition-based maintenance (CBM). The paper discusses how the TBM and CBM techniques work toward maintenance decision making. Recent research articles covering the application of each technique are reviewed. The paper then compares the challenges of implementing each technique from a practical point of view, focusing on the issues of required data determination and collection, data analysis/modelling, and decision making. The paper concludes with significant considerations for future research. Each of the techniques was found to have unique concepts/principles, procedures, and challenges for real industrial practise. It can be concluded that the application of the CBM technique is more realistic, and thus more worthwhile to apply, than the TBM one. However, further research on CBM must be carried out in order to make it more realistic for making maintenance decisions. The paper provides useful information regarding the application of the TBM and CBM techniques in maintenance decision making and explores the challenges in implementing each technique from a practical perspective.

A review of condition-based maintenance decision-making

Condition-based maintenance (CBM) has been a research topic since 1975. It has been introduced as an alternative approach to enhance the effectiveness of preventive maintenance strategy. Currently, CBM research is growing rapidly. Compared with the traditional time-based maintenance approach, CBM application is more beneficial and realistic. With CBM, better maintenance decisions can be made to avoid or minimise unnecessary maintenance costs. This paper attempts to explore how exactly CBM decision-making is conducted, with the methods of decision-making classified into current-condition-evaluation-based and future-condition-prediction-based. This paper systematically reviews the applications of these methods by focusing on the techniques used, as well as on case studies. It concludes with findings based on the academic and industrial perspectives. [Received 10 July 2010; Revised 9 November 2010; Accepted 24 February 2011]

Development of autonomous maintenance implementation framework for semiconductor industries

Total productive maintenance (TPM) is one of the maintenance strategies that aim to increase availability and reliability of production machines/equipment. The key to TPM success is the development of autonomous maintenance (AM) practice. The AM refers to human capital development among operators supported by technicians and engineers to perform easy daily maintenance activities aside from planned maintenance. This paper presents the implementation process of AM in a semiconductor company in Malaysia. An AM implementation framework is developed based on four systematic stages: AM initial preparation, AM training and motivation, AM five-step execution and AM audit. The framework is implemented in one of the production lines identified as the most critical area in the company chosen as the case study. The findings based on the developed AM framework are reported in this paper.

Preventive replacement schedule: a case study at a processing industry

The application of the preventive maintenance (PM) strategy to reduce machine breakdown problems has been discussed in many studies. One of the popular strategies that widely use it is preventive replacement (PR), which aims to determine the optimum replacement time. The critical issue, however, is that most studies assume the aging (lifetime) of a component to be time dependent. In reality, the failure of a component is influenced by an external factor (covariate). This paper presents the process of revising or updating the PR time by considering external factors (the covariates effect) by using the proportional hazard model (PHM). The revised PR time is determined based on the age replacement model (ARM) and block replacement model (BRM). The effects of the revised PR time on the reliability cycle and cost reduction for both ARM and BRM are compared. The results show that the application of ARM is more beneficial in terms of cost saving, while the application of BRM results in a higher reliability cycle. A corresponding case study in the processing industry is presented.

Implementation of dust control system using management and planning tools (MPT): A case study

Purpose – To introduce a research carried out in a real world for implementing a dust control system (DCS) for controlling the indoor air quality (IAP) on the production floor of one of the major electronics company in Malaysia.Design/methodology/approach – The paper is arranged as follows, a brief description of the significant of DCS in electronic industry and brief introduction to the electronic company as a case study company for introducing the DCS. The discussion on the characteristics management and planning tools (MPTs) that have been adopted as the analysing tools for assisting in the decision‐making process in identifying the problems and improvement strategies. It follows by the detail analysis phase regarding the implementation process that it as backbone for introducing the DCS. Finally a discussion about the result obtained from the MPT analysis on the techniques for identifying the root causes of the dust pollution problem as well as the best improvement strategies that can be adopted by th...

Structured maintenance engineering policy development based on a production machine process perspective

Purpose The purpose of this paper is to present the development of a maintenance engineering policy in the context of a decision support model based on a production machine process perspective. Design/methodology/approach The structure of the policy is called the maintenance decision support (MDS) model, which consists of three steps: initial setup, deterioration monitoring, and decision making. A detailed presentation of each step of the proposed model together with a real case example from the pulp manufacturing industry proves the applicability of the model. Findings Validation of the proposed MDS model is as follows. In Task 1 of Step 1, the cutting, sealing, and perforating line processes are classified as critical machining processes. The analysis of Task 2 of Step 1 found that cutting knife, bearing, and motor are classified as the components that most possibly contribute to the cutting appearance quality. In Task 3 of Step 1, it was found that the cutting knife is classified as a maintenance-significant component with non-repairable and single-component type characteristics. The result of Step 2 suggested that at the 29th hour of operating time, the decision of do-something was suggested. In the following step (Step 3), for the case of the cutting knife, which has been classified as a non-repairable type component, the decision to perform preventive replacement of cutting knife is recommended to be carried out at the 29th hour of operating time. Research limitations/implications The uniqueness of this model is that it systematically considers different machinery component(s) characteristics, including single- and multiple-component cases, repairable and non-repairable types, and functional or/and physical failure types, to make maintenance decisions. Practical implications The proposed MDS model provides a systematic guideline for identifying, evaluating, and monitoring, which makes maintenance-related decisions. Three significant maintenance decisions can be determined based on the proposed MDS model, which includes an appropriate time-to-perform maintenance, correct maintenance actions to be performed, and the right component required for maintenance (for multi-component cases). Originality/value One of the vital elements in considering the production machine process perspective toward the development of the MDS model is the need to use product output/quality characteristics for machine deterioration-monitoring and decision-making processes.

Application and comparison of three maintenance techniques for replacement decision making – a case study in the pulp manufacturing industry

The current paper presents the application of three maintenance techniques, namely, age replacement-based maintenance (ARBM), wear rate-based maintenance (WRBM), and output-based maintenance (OBM), for replacement decision making. The first two techniques are available in the literature, whereas the third one is a technique proposed in the present paper. The theory and principles underlying each technique are briefly discussed before the applicability of each is validated through a case study in the pulp manufacturing industry. The results of each maintenance technique for replacement decision making are compared and discussed from a practical point of view. The present paper concludes that OBM, the proposed technique, provides a more realistic approach towards replacement decision making compared with the other two techniques for functional failure type component. This conclusion is supported by a review of the entire process of replacement decision making based on four practicability criteria, namely, the basic theory and principle of each technique, data requirements and challenge in data collection, data analysis/modelling, and the decision process. Future research recommendations for the application of OBM are also given.

Maintenance decisions making method for repairable system by using output-based maintenance technique: A case study at pulp manufacturing industry

This paper presents a maintenance decision making method for the case of repairable system by using output-based maintenance (OBM) technique, which is an alternative technique under condition-based maintenance (CBM) approach. Under OBM technique, machine output measure such as product quality characteristic become the main monitoring parameter to indicate the deteriorating process and failure limit of the system. A simple decision algorithm model for the case of single-repairable system is proposed in order to suggest not only what the best maintenance actions to be taken but also when the right time to be performed. A case study at a pulp manufacturing industry has been presented to validate the maintenance decision making method. Results from the case study show that the proposed method is beneficial in making the maintenance decision for real industry case. The application of decision algorithm model through monitoring and future trend forecasting processes is able to suggest the right maintenance at the right time. This paper concludes with some uniqueness features of the proposed maintenance decision making method, contributions and the future works.