This paper develops a model-based technique based on the Unscented Kalman Filter (UKF) for on-line condition monitoring, and applies it to the hydraulic system of an automated industrial fish processing machine. First an analytical model of the hydraulic system is developed and the system parameters are identified (determined). Then the developed UKF approach is implemented in the machine. The UKF employs an unscented transformation to select a minimal set of sample points around the mean, which are then propagated through nonlinear functions, from which the mean and covariance of the estimate are recovered. This approach is known to be more accurate for nonlinear systems. For experimental investigation of the performance of the approach, four common hydraulic faults are deliberately introduced into the machine. The four faults are external leakage in the two chambers of the hydraulic cylinder; internal leakage; and dry friction build-up at the moving support plate of the cutter carriage. Three levels of leakage are manually introduced to the system for each fault scenario using needle valves. The criteria that are considered in fault diagnosing are residual moving average of the errors, chamber pressures, and actuator characteristics. The experimental results show that the developed technique is able to accurately determine the fault conditions of the machine.

Abstract. With stringent standards for materials, manufacturing, operation, and quality control, jet engines in use on commercial aircraft are very reliable. It is not uncommon for engines to operate for thousands of hours before being scheduled for inspection, service or repair. However, due to required maintenance and unexpected failures aircraft must be periodically grounded and their engines attended to. The tasks of maintenance and repair without optimal planning can be costly and result in prolonged maintenance times, reduced availability and possible flight delays.This chapter presents the development of Discrete Event Simulation (DES) models that utilize aircraft flying, grounding and engines service times, as well as Time-On-Wing (TOW) data which represents the current accumulated flying time for each engine since its last service, and Remaining-Time-to-Fly (RTTF) to aid maintenance policy decision making. The objective is to determine the optimum number of engines on an aircraft for maintenance that leads to greater use of the estimated remaining useful life of the engines and shorter downtime for the aircraft. To achieve this, first, a number of small models are built and simulations performed to gain an insight into the problem. A final model is then developed that is based on the integration of these small models. It is shown that a simulation model of this type can enable the decision maker to readily examine different policies and from the analysis of the simulation output arrive at an optimum policy.

The tasks of maintenance and repair without optimal planning can be costly and result in prolonged maintenance times, reduced availability and possible flight delays. Aircraft manufacturers and maintainers see significant benefits in constantly improving Health Management and Maintenance (HMM) practices by deploying the most effective maintenance planning strategies. The planning of the maintenance and repair is a complex task due to chain dependency of engines to aircraft, and aircraft to the flight schedules. This paper presents a scheduling method for determining the time of maintenance based on the historical engine operation data in order to maximize the use of estimated remaining useful life of the engines as well as lowering the cost and duration of the downtime. The Time-on-Wing (TOW) data is used in conjunction with probability density functions to determine the shape of the respective distribution of the time of maintenance to minimize the loss of expected remaining useful life. Data from each engine with most chance of failure is then selected and fed into an extended Branch and Bound (B&B) routine to determine the best optimum sequence for entering the facility in order to minimize the waiting time.

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