System throughput is one of the most important measure of process capability. Most organization optimize their processes to avoid waste in line with the total quality management philosophy but they never factor in the impact of system downtime as a result of equipment failure or process failure. While Okoh, Roy & Mehnen (2017), proposed that organizations should adopt a proactive machine or equipment maintenances, the current focus is on how to optimize equipment or machine output by eliminating equipment or machine failure. Predictive maintenance is a machine management philosophy whose objective is predicts machine failure by monitoring the condition of the machine and the performance of the same as the machine is in operation. Monitoring machine and predicting the likelihood of machine failures help the firms to reduce loss as a result of machine failure, reducing the frequency of machine maintenances exercises, and eliminating unplanned machine breakdowns. Predictive maintenance is also known to eliminate minor preventive maintenance. Just like with small, machines, lubrication, corrosion, and parts ware out should be monitored and test. However, most advanced organization has adopted sophisticated testing techniques including infrared testing, acoustic testing and vibration analysis. Currently, companies such as Toyota use computerized maintenance management systems (CMMS) for machine condition monitoring. Based on the increase in the adoption of predictive maintenance, the aims of this study are:
- To determine the main drivers of the adoption of predictive maintenance in manufacturing firms
- To determined the effectiveness of the predictive maintenances in comparison to the alternative methodologies such as preventive eminences
- To investigate the best practices of predictive maintenance.
- To analyze models of predictive maintenance
- Recommend or develop a novel model for predictive maintenance
While many organization adopt the run to failure maintenances or run to fail strategy, the loss from machine breakdown, unearned income is usually costly and might lead to loss of customers or loss of customer confidences in the firm's ability to deliver,. Therefore reactive maintenances are not a good strategy. Companies must define their acceptable boundaries for equipment operations and most proactive firm have the documentation on the readings, graphs and automated systems that trigger email notification when machine deteriorates to a specific level. As opposed to preventive maintenance that relies on the expected life of the asset before maintenance is initiated, the predictive maintenances is determined by the condition of the machine. For example predictive maintenance can rely on the vibration measurements of the electric engine to help in predictive wear and tear
Predictive maintenances involve the use of prognostics to make maintenance decisions. Prognostics in predictive sentence can help organization to maximize the utility of their machines by detective anomalies and initiating corrective measures or maintenance before the machine finally breaks down. The remaining useful life has often been the most acceptable benchmarks for determining when to initiate maintenances. Prognostics help in predicting the future performances of a machines or its component by assessing the degree f deviation from the expected operating conditions. The most common technical approaches to developing prognostic model include data-driven, over model-based approaches. In most companies the hybrid approaches involving both data-driven and model-based approaches are employed. While data drive/ statistical model parameters offers a sound model for predictive maintenance, it is also important to understand that the successful parameter estimation and the application of the parameter estimation methods to predict the important statistical models.
Figure 1: Flowchart for prognostics modelling
Many kinds of literature have been presented on the concept of predictive maintenance. Works of literature are based on the current and past research consider the concept to be very important in the engineering context. Al-Jubouri and Gabrys (2014) considered predictive maintenance as an interventional measure that covers a number of techniques whose are investigate on the in-service equipment and determine when maintenance practices should be applied. Zhang, Wu, Bie, Mehmood and Kos (2018) argued that the approach has been used in the engineering context to save on cost by ensuring that maintenance, which may emerge costly, are only undertaken when necessary. Zhang, Wu, Bie, Mehmood and Kos (2018) added that it also serves as a strategic exercise through which an organization prevents the unexpected occurrence of failures, by creating schedules of corrective maintenance. According to Wilson, Filion and Moore (2015), it has enabled successful planning of maintenance by considering the need to have the right information at the right time. However, Valis, Hasilova, Forbelska and Pietrucha-Urbanik (2017) pointed out that it may be a challenging task for the approach to help an organization to establish flexible programs of planning maintenance. Otherwise, it has the potential to ensure that fewer accidents occur in a given plant, prolonged spare lifetime and reliability of the primary activities of the firm in question.
Predictive maintenance, with regard to the basis of prediction, differs from the preventive maintenance due to its reliance on the specific condition of the equipment, rather than the expected or average statistics of life (Scheidegger, Leitao and Scholten, 2015). There are necessary components which make the basis of a complete predictive maintenance process. Samuelsson, Bjork, Zambrano and Carlsson (2017) showed that these include data management which involves the collection and preprocessing fault detection, occurring at two stages, prediction of the failure, maintenance schedule as well as resource optimization. It has enabled firms to increase productivity by working on the principle of just in time production of products. By evaluating the conditions of equipment at both the offline and online context, has been used to manage equipment properly. According to Martin and Vanrolleghem (2014), the proper management ensures that they are in the right condition when they are needed for a given important operation. Consequently, the flow of operations under the use of the equipment is not interrupted but rather proceeds as planned (Mala-Jetmarova, Sultanova and Savic, 2018).
Water Distribution Systems (WDS) Reliability
According to Kabir, Tesfamariam and Sadiq (2015), predictive maintenance is applicable in the management of water distribution systems in which the focus is on the reliability. The intervention recognizes the fact that every water distribution system has to fail completely or partially at a given point or points during its lifetime. Measurement of WDS reliability has been used as the strategic way of incorporating the concept of predictive measurement. Garcia-Alvarez (2018) noted that the reliability in this context refers to the ability of the WDS to maintain both qualitative and quantitative water supply to the consumers irrespective of the circumstance. It is therefore important to understand this aspect of WDS reliability to allow engineers to determine the type of adjustments to be applied and when to apply them (Harvey, McBean and Gharabaghi, 2014) A number of methods have been approved for estimating the reliability of WDS. A deeper understanding of the methods implies that an individual or organization understands the existence and implications of the vari...
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