To deal with the lack of good quality and commonly accepted fault data of large-scale equipments, simulation based strategy is calling for more and more attention. This paper focuses on the real-time fault injection method in the simulation model of mechanic-electronic-hydraulic control system. First, simulation model of a ship steering system was built in AMESim. And then, several typical faults were injected in the established model. To realize the real-time fault injection, this paper utilized LabVIEW to cooperate with AMESim to carry out Cosimulation. Analysis result showed that the acquired simulation data of different injected faults corresponded with theoretical estimation well. Work in this paper provides an available approach to get fault data in real-time working condition for safety analysis.

Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME) systematic safety field that studies faults in the steering system itself, the driver's human reliability (HR) and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment.Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF) risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.

A new non-aqueous and abrasive-free magnetorheological finishing (MRF) method is adopted for processing potassium dihydrogen phosphate (KDP) crystal due to its low hardness, high brittleness, temperature sensitivity, and water solubility. This paper researches the convergence rules of the surface error of an initial single-point diamond turning (SPDT)-finished KDP crystal after MRF polishing. Currently, the SPDT process contains spiral cutting and fly cutting. The main difference of these two processes lies in the morphology of intermediate-frequency turning marks on the surface, which affects the convergence rules. The turning marks after spiral cutting are a series of concentric circles, while the turning marks after fly cutting are a series of parallel big arcs. Polishing results indicate that MRF polishing can only improve the low-frequency errors (L>10  mm) of a spiral-cutting KDP crystal. MRF polishing can improve the full-range surface errors (L>0.01  mm) of a fly-cutting KDP crystal if the polishing process is not done more than two times for single surface. We can conclude a fly-cutting KDP crystal will meet better optical performance after MRF figuring than a spiral-cutting KDP crystal with similar initial surface performance.

Abstract-Lack of historical fault data in real-time working condition is a pivotal problem in safety analysis of large-scale mechanic-electronic-hydraulic systems. To deal with this problem, this paper proposed a real-time fault injection and simulation method based on virtual prototyping. First, the virtual prototyping of actual system was built in AMESim and its credibility was verified and validated. On this basis, several typical faults were injected in the virtual prototyping. To realize real-time fault injection, this paper utilized MATLAB/Simulink to carry out Co-simulation with AMESim by designing I/O interface. Steering system was taken as case study to verify the proposed method. Simulation result showed that the acquired simulation data is credible and available.

For the fault diagnosis of mechanic-electronic-hydraulic control system (MEHCS), the main barrier that restricts the application of knowledge-based methods is the lack of historical fault data. Aiming at this problem, this paper proposed a hybrid fault diagnosis method based on simulated knowledge from virtual prototyping. As a special form of mathematical model, virtual prototyping of MEHCS under faulty and nominal condition was established, validated, fault-injected and simulated to obtain simulation data. Fault features of different fault types were extracted, which were then trained by three pattern recognition methods to build the knowledge database for diagnosis. Threshold test and ensemble classifier constituted by the three pattern recognition methods were employed respectively to realize fault detection and isolation. To verify the proposed methodology, a case study of vessel steering system was presented. Fault types of stuck rudder and steady state error were studied. Probabilistic neural network (PNN), naive Bayes (NB), and k-nearest neighbor (kNN) were employed to constitute ensemble classifier based on majority voting. The diagnosis results showed that the accuracy of fault detection and isolation of both fault types were highly acceptable. The ensemble classifier performed better on comprehensiveness and smoothness than any individual pattern recognition method for the overall diagnosis. The proposed method might be an available choice for the fault diagnosis of MEHCS, especially for large-scale and complicated cases.