F. Afsharnia; A. Marzban
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 ...
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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.
O. Omidi-Arjenaki; D. Ghanbarian; M. Naderi-Boldaji; K. Mollazadeh
Abstract
Introduction The texture of fresh fruit is determined by the structural and mechanical properties of tissue. It depends on climate, maturity, variety and postharvest condition. During ripening, due to loss of turgor, degradation of starch and cell walls, the flesh of apple softens. The relationship between ...
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Introduction The texture of fresh fruit is determined by the structural and mechanical properties of tissue. It depends on climate, maturity, variety and postharvest condition. During ripening, due to loss of turgor, degradation of starch and cell walls, the flesh of apple softens. The relationship between fruit quality and its physiological changes has been widely investigated. Using techniques according to the principles of force-deformation, impact, and vibration tests, texture of fruit and its mechanical properties can be associated, conventionally. In analyzing the vitality of biomaterials; a non-invasive technique based on the optical phenomenon is the Biospeckle method which occurs when the surface of the sample is illuminated by laser light. It seems that because of the fact that the laser light can penetrate tissue, it is possible to obtain information about the texture and cell condition from tissue under the skin. This means that, there would be a chance to detect and monitor the variation of cells and try to make a model to predict mechanical properties. Therefore, the overall objective of this study was to develop prediction models based on biospeckle imaging to predict mechanical properties of ripe Golden Delicious apples. Materials and Methods The 400 fresh and intact 'Golden Delicious' apples were harvested and were prepared for mechanical tests and biospeckle imaging. Biospeckle imaging was carried out first, followed by compression and creep test and then penetration test. During imaging, to avoid environmental reflections, the process was carried out in a dark and closed chamber. Biospeckle activity was saved as a video (AVI format) in a computer for analyzing. The THSP method was used to analyze biospeckle activity in samples. The indices which have been used for analyzing biospeckle images are divided into 3 statistical features and 4 textural features. Apples were cut in half. One of the halves was used for cylindrical sample extraction for uniaxial compression and creep tests and another was used for penetration test. From compression tests the tangent modulus of elasticity, stress and strain of bio-yield and failure energy for toughness calculation were determined. The creep behavior was obtained by fitting the Burger's model to the experimental data. In penetration test, a stainless steel probe with a hemispherical tip was used for peeled and unpeeled samples. For each sample maximum penetration force and energy were obtained. Prediction of mechanical property was carried out using adaptive neuro-fuzzy inference system (ANFIS). To reduce the dimension of the input vector the PCA was used. Four significant adjustments were made in the structure of ANFIS in order to find the best models. The models were evaluated using RMSECV, RMSEP, MBEC, MBEP, RC, and RP. Results and Discussion Models for modulus of elasticity prediction have Rp=0.821, 0778, 0.791, 0.880, and 0.843 for 4 compression rate and secant modulus, respectively. Clearly, the results from this research are encouraging, indicating the potential of using speckle imaging system for predicting apple fruit mechanical properties. Comparing to the all texture analysis techniques, Wavelet and GLRLM provided good results for most properties leading to select them as the best techniques for analysis of biospeckle images because of their consistency in prediction performance. Prediction model for break strain has the highest Rp (Rp=0.920) followed by the retarded time (Rp=0.890), retarded viscosity (Rp=0.886) and maximum penetration force in unpeeled case (Rp=0.883). A lower correlation (Rp = 0.728) was observed for initial viscosity. Conclusion The described optical method based on biospeckle represents an innovative and reliable method for rapid and non-invasive detection of mechanical properties. The results of the evaluation showed that, as time passes, fresh apples due to the loss of water in both the elasticity and the biospeckle activity were dropped. Biospeckle imaging can accurately predict mechanical properties. The average accuracy of best prediction of mechanical properties models was R2=0.899. The present results can provide the basis of future development of in-line quality monitoring during apple quality control.
