Multivariate Approach to Bench Marking Quality Prediction Parameters in Building Maintenance Works
CHAPTER ONE
Objective and Research Question
Accordingly, this thesis aims to answer the following research question: 1. What are the impacts of implementing Condition-based maintenance policies in a buildings maintenance context? Furthermore, to comprehensively achieve the aim of the thesis and support the main research question, the following sub-questions have been developed for investigation:
- What are the costs, savings and opportunities of implementing CBM?
- What effect does incorporate real-time vibration analysis have on an existing time based maintenance regime?
- What statistical association do plantroom temperatures, relative humidity and asset energy consumption have on the occurrence of faults?
Therefore, the objectives of this thesis are as follows:
- Undertake a feasibility study to determine key costs, savings and potential opportunities of implementing predictive maintenance (online vibration condition monitoring).
- Implement online vibration monitoring on critical rotary building assets to establish viability and practicality of predictive maintenance.
- Collect and statistically analyse data relating to:
- Hours of operations, in order to provide insight into the operations strategy and inform maintenance and life cycle decision.
- Consumption of electricity, in order to establish whether an association between fault and higher consumption exists.
- Atmospheric temperature and humidity, in order to ascertain the environment within which the assets operate
CHAPTER TWO
LITERATURE REVIEW
Maintenance is not a new idea, however (as demonstrated by Figure 2) the sphere of maintenance management is constantly changing (Kobbacy & Murthy 2008). Consequently, there is an immense amount of literature on maintenance, which spans over fifty years. The underlying facets and the associated key literature are discussed below.
1940s and 1950s – Necessary Evil: Nearly all maintenance actions were corrective (actions applied after a failure has occurred) with a mentality of ‘if it isn’t broke, don’t fix it’. Consequently, maintenance was generally considered as a necessary evil with annoying, inevitable and unmanageable costs (Pintelon & Parodi-herz 2008; Kobbacy & Murthy 2008; Ahmad & Kamaruddin 2012; Shin & Jun 2015). However, a small minority of pioneering organisations (i.e., Rio Grande Railway Company and US Army) started to investigate data monitoring with a goal of technically understanding and preventing failures (Martin 1994; Prajapati et al. 2012). Literature focused on mathematically angled reliability research (Pintelon & Parodi-herz 2008).
1960s and 1970s – Technical Matter: Progressively, companies started to acknowledge that certain failures of mechanical assets were caused by preventable age-based fatigue and/or operational degradation (Pintelon & Parodi-herz 2008). To combat such failures, precautionary or preventive maintenance actions were commonly scheduled by companies with the mentality that cost savings could be achieved in the long term from averting the failures (Al-Najjar 2012; Pintelon & Parodi-herz 2008; Ahmad & Kamaruddin 2012; Kobbacy & Murthy 2008). However, cost-effectively regulating the frequency of the actions was challenging, especially as the understanding of historic failure patterns and data were limited. Consequently, maintenance became a ‘technical matter’ where engineering and statistics were applied to achieve a greater understanding of patterns, thus creating the ‘Reliability Engineering’ branch of maintenance research (Pintelon & Parodi-herz 2008; Holmberg et al. 2010; Prajapati et al. 2012).
While the main literature focus remained mathematical, the orientation was now based on modelling policy optimisation (i.e. to determine the optimum preventative frequency or interval), however these theoretical models lacked realistic hypotheses and therefore were difficult to apply within industry resulting in an unfortunate gap between academics and practitioners (Prajapati et al. 2012; Pintelon & Parodi-herz 2008; Brown & Sondalini 2013). Moreover, the influence of optimising technical solutions via statistically considering engineering (maintainability at the design and development stage), science (achieving better understanding of material degradations), and reliability (probability patterns) in parallel with economical, legal and operational applicability instigated perceptions to be progressively shifted and alternative methodologies to be developed (i.e. Predictive or Condition Based) (Prajapati et al. 2012; Martin 1994; Pintelon & Parodi-herz 2008). Additionally, as reliability knowledge improved in the late 1970s, the effectiveness and the deceptive benefits of exclusively applying preventative actions on all assets (simple and complex) began to be doubted with growing apprehensions of ‘over-maintaining’ assets unnecessarily (Kobbacy & Murthy 2008; Pintelon & Parodi-herz 2008). This steadily triggered a change in direction and uptake of predictive maintenance actions based on condition monitoring (Pintelon & Parodi-herz 2008; Prajapati et al. 2012). However, completely shifting to predictive maintenance was limited to ‘high-risk’, technically feasible and economically beneficial applications (such as nuclear power plants and aviation – aided by the introduction of Boeing 747s). Although in the early 1980s, as monitoring equipment became cheaper and accessible, the techniques started to be utilised outside of ‘high-risk’ domains such as manufacturing (Tinga 2010; Pintelon & Parodi-herz 2008; Ahmad & Kamaruddin 2012).
CHAPTER THREE
RESEARCH METHODOLOGY
Introduction
This chapter covers the description and discussion on the various techniques and procedures used in the study to collect and analyze the data as it is deemed appropriate
Research Design
For this study, the survey research design was adopted. The choice of the design was informed by the objectives of the study as outlined in chapter one. This research design provides a quickly efficient and accurate means of assessing information about a population of interest. It intends to study the multivariate approach to bench marking quality prediction parameters in building maintenance works. The study will be conducted in Abuja metropolis.
CHAPTER FOUR
DATA ANALYSIS AND INTERPRETATION
Introduction
This chapter deals with the presentation and analysis of the result obtained from questionnaires. The data gathered were presented according to the order in which they were arranged in the research questions and simple percentage were used to analyze the demographic information of the respondents while the chi square test was adopted to test the research hypothesis.
CHAPTER FIVE
This research was conducted within a large, operational public sector building in the UK that is part of a long-term PFI contract with a maintenance budget of circa £4 million per annum. More specifically, the research was focused on critical rotary HVAC system components namely, 83 individual motors, pumps and AHU supply and extract fans. Such rotating assets are prevalent within the built environment and form the foundation of building services engineering and building maintenance management protocols, which promotes relatability and transferability of this study. The empirical findings from this study relate to the main research questions and objectives, which set out to enhance the background research (see Amin & Pitt, 2014) and ascertain the impacts of implementing online CBM policies and statistical data analysis in building maintenance context. In exploring the main research question three sub-questions were developed and investigated. First, the cost, savings and opportunities of implementing CBM were established through a comprehensive technical feasibility and cost benefit analysis. The survey of literature relating to maintenance management highlighted that CBM appeared to be reserved for high risk and high value assets such as military, aeronautics industries and critical manufacturing plant. Moreover, CBM is recognised as an expensive maintenance strategy that rarely provides the economical justifications. Therefore, this investigation implemented an exclusive mixed-method data collection methodology that was further supported by the action research platform of monthly EngD board meetings (involving academics and professionals) to ensure collective and iterative scrutiny of the analysis and findings. Moreover, to enable the comparative analysis to identify the savings and opportunities, the relevant condition monitoring data acquisition costs were acquired through three specialist external consultants and the most economical option was used for the financial justification analysis. The comparative financial analysis was conducted based on the existing time-based maintenance policy and a proposed concept that was inclusive of CBM polices enabling predictive and proactive maintenance actions. Furthermore, the analysis considered the most significant business impacts in respect to the application of condition monitoring, OPEX and CAPEX over the total remaining life of the PFI contract (sixteen-years). The findings indicate that the proposed third-generation maintenance solution, which amalgamates time-based actions with CBM policies, has the potential to provide an OPEX saving opportunity of £541,464.14 and a CAPEX savings opportunity of £250,000. Additionally, it would generate be a variety of managerial and service operational benefits and opportunities that are challenging to quantify at this stage. These unquantifiable benefits and opportunities relate to risk management, reduction in asset downtime, service quality improvements and informed asset life cycle decision-making.
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