Development Of Condition Based Maintenance Research Articles

Comparative performance and emissions assessments of a single-cylinder diesel engine using artificial neural network and thermodynamic simulation

Diesel engine parameter prediction became a topic of interest in recent years, along with the development of condition-based maintenance, and is now considered a key instrument for engine diagnosis research. This contribution compares two different approaches for diesel engine performance prediction: thermodynamic modelling and artificial neural networks (ANNs). The thermodynamic modelling was developed using AVL Boost™ software simulating a single-cylinder diesel engine with different engine loads and operating conditions. The ANN modelling was conducted by comparing two efficient training algorithms to achieve the best prediction performance, with the ANN structure parameters determined by network error analysis. Both models’ prediction accuracy was verified by a single-cylinder engine test bench operating under real conditions. The adaptability and robustness of the two approaches was studied for the whole engine load spectrum, comparing predicted values to experimental measurements. Both prediction tools, ANN and thermodynamic modelling, proved to be reliable for engine performance and emissions prediction. In both models brake-specific fuel consumption (BSFC), exhaust gas temperature (Texh), carbon monoxide (CO) and nitrogen oxides (NOx) were predicted using brake mean effective pressure (BMEP) and engine speed as inputs. ANN show higher accuracy for BSFC prediction in all engine loads, and Texh prediction accuracy is better for ANN when dealing with medium to high loads, while the thermodynamic model shows better results when dealing with medium to low loads. CO is better predicted by the thermodynamic model except for the highest engine loads, and NOx predictions present high accuracy in both models, except for the lowest loads. Calculation time is lower for ANN, but the thermodynamic model provides additional performance results (i.e. combustion pressure tracing and associated values).

Prognosis and Diagnosis of Farm Tractors Reliability and Availability for Maintenance Policies Using Markov – Chain Model

A sound maintenance planning is of crucial importance for farm power systems. There is a large potential in cost savings by optimizing maintenance decisions to make utilization of farm tractors more cost-efficient. Reliability and availability are fundamental attributes of organization, scheduling and operation of fleet of tractors in agricultural project of multi-farms. This paper utilizes recursive Markov chain closed-form analytical solution and condition-based maintenance model to evaluate performance of degraded multi-state system. First state of system failure is inspected, analyzed, and classified into partial, or combined or complete failure of estimating the transition matrix for the failure state. At each inspection of failure status a preventive maintenance (minor repair by replacement of parts) or corrective maintenance (major replacement of parts by complete overhaul) is performed to restore the system to good as new. The development of condition-based maintenance is used to signify the monitoring of machines for the purpose of diagnostics and prognostics. Diagnostics are used to determine the current status of a machine's frequency of failure (useful life) and prognostics are used to predict its dependability, availability (utility).Hence, the system of evaluation is quantified by six distinct indicators (maximum time before failure, tractor dependability, availability, frequency of failure and operating time between preventive and corrective maintenance) such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. Simulation results for a dataset of three tractors (T120, C225 and B250) from two workshops of sugar plantation (Gunied and Sennar factories in Sudan) is investigated to assert the magnitude of variation between the tested variables that justify changing current maintenance policy using analysis of variation. The results indicate the applicability of Markov where comparison with condition-based maintenance is the optimal maintenance strategy for tractor high failure rate.

Maintenance Cost Optimal Model for Failure Dependence

With the development of Condition-Based Maintenance, there is an urgent need for failure dependence of redundant components in parallel system. In this paper, Markov maintenance cost decision analysis based on failure dependence of components was put forward, moreover, maintenance cost optimal model of parallel system with cost rate was established, meanwhile, the minimum cost rate as well as corresponding optimal detection interval was obtained. Finally, failure dependence and dependent failure rate to parallel system maintenance cost optimal model were studied and realized to prove this theory.

A MODEL FOR THE MAINTENANCE OF OLD AIRCRAFT

This paper presents an approach to the development of condition-based maintenance of aircraft to improve aircraft maintenance in the limits and conditions of small countries and in accordance with global technological achievements. For the aircraft that were under a limited lifespan maintenance concept, a new condition-based maintenance model is developed. This model applies a technical diagnostic method that brought us to decreasing numbers of failures in the ageing period of those technical systems and at the same time decreases the time when it is out of use and lowers maintenance expenses. An analysis based on this model showed its applicability, preservation of operational readiness, and potential for decreasing the cost of aircraft maintenance.

Testing automobile engines electrically

Diesel engine parameter prediction became a topic of interest in recent years, along with the development of condition-based maintenance, and is now considered a key instrument for engine diagnosis research. This contribution compares two different approaches for diesel engine performance prediction: thermodynamic modelling and artificial neural networks (ANNs). The thermodynamic modelling was developed using AVL Boost™ software simulating a single-cylinder diesel engine with different engine loads and operating conditions. The ANN modelling was conducted by comparing two efficient training algorithms to achieve the best prediction performance, with the ANN structure parameters determined by network error analysis. Both models’ prediction accuracy was verified by a single-cylinder engine test bench operating under real conditions. The adaptability and robustness of the two approaches was studied for the whole engine load spectrum, comparing predicted values to experimental measurements. Both prediction tools, ANN and thermodynamic modelling, proved to be reliable for engine performance and emissions prediction. In both models brake-specific fuel consumption (BSFC), exhaust gas temperature (Texh), carbon monoxide (CO) and nitrogen oxides (NOx) were predicted using brake mean effective pressure (BMEP) and engine speed as inputs. ANN show higher accuracy for BSFC prediction in all engine loads, and Texh prediction accuracy is better for ANN when dealing with medium to high loads, while the thermodynamic model shows better results when dealing with medium to low loads. CO is better predicted by the thermodynamic model except for the highest engine loads, and NOx predictions present high accuracy in both models, except for the lowest loads. Calculation time is lower for ANN, but the thermodynamic model provides additional performance results (i.e. combustion pressure tracing and associated values).