Investigating The Long-term Effect of Preventive Maintenance Strategy on The Operational Efficiency and Failure Rate of Sugarcane Harvester Using Time Series

Abstract

Introduction Optimal operation and maintenance of engineering systems heavily relies on the accurate prediction of their failures. Repairable engineering systems are well known in industries. A repairable engineering system indicates that the performance of this system after each failure can be restored through suitable maintenance. It is normally a complex system composed of a number of components. Failure prediction of a repairable system and its subsystems is an important topic in the reliability engineering. One of the most important repairable systems in agro-industrial companies is the sugarcane harvester. This machine has a key role in harvesting operations of sugarcane plant. The failures of this machine causes delay in operations and reduce products yield and quality. Currently, preventive maintenance is conducted on these harvesters to improve the overall reliability of these systems. Therefore, in this study, the long-term effect of preventive maintenance strategy on the efficiency and failure rate of the sugarcane harvester was investigated. Materials and Methods This research was carried out on 30 sugarcane harvesters used by sugarcane and by Products Development Company of Khuzestan during 6 years period. The goal of this study was to introduce a methodology aimed to acquire the information to predict the effect of preventive maintenance strategy on the failure rate and efficiency of sugarcane harvester by time series. Time series forecasting is the use of a model to predict future values based on previously observed values. The expected shape is a forecast from a combination of an ARIMA models (AR, MA, ARMA and ARIMA). The first step in analyzing the time series is plotting the data and obtaining the sample records. The next step is consideration of a trend and periodic components and remove them from the time series and fitting the static model on the time series. The next stage is implementation of the data normalization using skewness coefficient method and their normalization through logarithm differentiation of data. The arithmetic mean of data was applied to obtain zero average of the time series. Sample ACF (Auto Correlation Function) and PACF (Partial Auto Correlation Function) was drawn and then the model rank "a" was recognized and selected by comparison of ACF and PACF for AR, MA, ARMA, and ARIMA models. Results and Discussion According to the results of failure rate related to the sugarcane harvester, it can be found that the mean failure rate of this machine for the 6-years period was equal to 0.147 per hour. The minimum and maximum value of the failure rate were 0 and 0.517 per hour, respectively. The mean annual use hours of these harvesters was 189.8 h. Although the accumulated used hours increased, the mean time between failures (MTBF) was increased. According to Jacobs et al. (1983), the machines may breakdown due to a design defect, physical damage, or normal wear and tear, but many times machines fail because of a neglect and the lack of properly scheduled maintenance. In this study, implemented preventive maintenance resulted in decreasing of failure rate and increasing of machine efficiency as well. In 2016, the failure rate of sugarcane harvester was decreased by 73.23% and the machine efficiency was increased by 14.9% compared to 2011, because timely preventative maintenance and inspection will not only help to reduce major problems and downtime, but it will also help to identify problems when they can be corrected with relatively minor repairs. Among the 12 studied subsystems, topper, electric and motor subsystems were more affected by preventive maintenance by 94.75%, 80.46% and 58.74% decreasing in the failure rate, respectively. With regard to the evaluation criteria such as AIC, MAPE and RMSE, the ARIMA (1, 3, 2) model was determined as a suitable model for predicting the failure rate of sugarcane harvester. Furthermore, there is no significant difference between statistical descriptive measures of forecasting and actual tractor failure rate that it represents high accuracy of forecasting via ARIMA model. Conclusion This study was adapted to the preventive maintenance as a useful strategy that could increase availability and operational efficiency of the sugarcane harvester. Furthermore, it focused on time series modeling method to analyze and forecast the reliability characteristics such as the expected number of failures per interval (failure rate). It is found that time series model is a viable alternative that gives satisfactory results for interval failure predictions in terms of its predictive performance for the sugarcane harvester reliability.

Keywords

• sugarcane harvester
• forecasting
• arima
• efficiency