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In order to predict the reliability of semiconductor lasers, an accelerated degradation test (ADT) was proposed as an element of reliability testing. Temperature-stressed ADT was applied for 8 Semiconductor lasers which used in space missions, and the degradation characteristics of output power of semiconductor lasers were studied. Then, a reliability model based multi-output Gaussian process regression (MOGP) was proposed to evaluate the lifetime and reliability for laser diodes. The advantage of the proposed MOGP based method is that it utilizes the output correlation between multiple degradation traces to make the outputs utilize each other's information and provide more accurate prediction than single modeling. Thereby improving the prediction accuracy. Furthermore, verifying applications and cases studies are discussed to prove the generality and practicability of the proposed reliability prediction model. Results show that the accuracy of the proposed MOGP based method is twice that of the SVM method.

As equipment becomes more and more complex, it is increasingly difficult to manually extract and select fault features manually based on expert experience or signal processing techniques. In addition, the shallow model such as BP neural network and SVM have trouble to deal with the complex mapping relationship with respect to the measured signal and the health condition of the equipment, who faces the problem of dimensional disaster. Combined with the advantages of deep confidence network (DBN) in features extraction and deal with high-dimensional and nonlinear samples, a fault feature extraction and diagnosis method based on deep confidence network for gearbox is investigated in this framework. The method uses the original time domain signal to train the deep confidence network and completes the intelligent diagnosis through deep learning. The preponderance is that it can take out the dependence on a great quantity of signal processing techniques and diagnostic experience, and accomplish the extraction of fault features and the intelligent diagnosis of health status with the characteristic of self-adaption. The method has no periodic requirements for time domain signals, and has strong versatility and adaptability. The experimental results of the fault diagnosis for the planetary gearbox demonstrated the feasibility and superiority of the presented method.

The main factor, which leads to semiconductor lasers degradation in space mission experiments, is the introduction of lattice defects in the detector material produced by radiation. Therefore, a new remaining useful life (RUL) prediction method considering radiation effect is presented. The prediction method is based on failure mechanisms and particle filters, which realizes the combination of a physical model and data-driven method. The particle filters can abundantly employ degradation data and avoid ignoring the failure mechanism. Accelerated degradation testing (ADT) is proposed for effectively assessing the proposed prediction method, in which the proton radiation is selected as the accelerated stress. The prediction results are compared with other methods to verify the accuracy and effectiveness of the proposed prediction method.

To solve the problem of difficulty in extracting fault features from the vibration signal of centrifugal pump, this paper proposes an integrated method based on multi-scale entropy (MSE) and local characteristic-scale decomposition (LCD) for centrifugal pump fault diagnosis. Firstly, LCD is applied to decompose the signal into a series of components. Secondly, MSE is extracted as the signal feature. Thirdly, probabilistic neural network (PNN) is employed for fault recognition. Finally, a case is studied based on the experimental data of a centrifugal pump, and the present method achieves the accuracy of 98.00%. Compared with other fault diagnostic methods, the proposed approach exhibits the best performance. The results validate the effectiveness and superiority of the present method.

With the continuous progress of the space station project, the rotary mechanism is widely used in the space station. Serious failure of rotating mechanism can cause motor burnout and then safety problems. Therefore, it is necessary to monitor and diagnose rotating mechanism. The vibration signal of the rotating mechanism is nonlinear and non-stationary. Therefore, the acceleration sensor vibration signal is decomposed by CEEMD method to get the intrinsic mode function. Next, Fourier transform is used to obtain time-frequency information according to the obvious components of fault characteristics, and the information entropy of time-frequency information is calculated. The degree of dimensionality reduction about fault feature is determined by selecting the number of principal components through adaptive cumulative contribution. Finally, the fault feature is trained by support vector machine and tested with data. Experimental results show that the optimized method is less computational and can accurately extract fault feature information.

With the increase of equipment complexity, the probability of equipment failure is greatly increased. A wide range of engineering practice shows that the equipment investment in the different stages had a profound impact on the later maintenance costs. Therefore, it is very necessary maintainability to do maintenance optimal designing. But most of the existing maintainability design is a qualitative design. In accordance with the existing maintainability design theory and method, the maintenance requirements are hard to produce sufficient constraints on product design. This paper solves the layout design problem of maintainability without reference standard. It also provides the theoretical basis for the layout designer. This paper uses quantitative method of maintainability design to guide the maintainability design of product. This paper will solve the above problems through the rational layout of the maintainability.

Virtual reality technology is a new way for people to visualize and interact with complex data through computers. Virtual reality technology based on simulation software is relatively mature. However, the simulation process is more complicated and the simulation of the interactivity cannot be satisfactory. Immersive virtual objects in the virtual scene are more interactive, and more in line with the laws of physics, and more consistent with the real world. The interactive features of virtual maintenance objects are expressed in a variety of ways. It uses modeling and provides constraint method to express the inherent attribute, object such as size, color, geometry. At the same time, we use software to correct the picture of the separation and the non-synchronization caused by the vertical relationship between screens. This paper uses a variety of techniques to achieve the acquisition and expression of virtual interactive features.

Digital television middleware is a new research field of digital television. It is a software platform in the receiver of digital television broadcasting. It allows various set-top boxes to be used in digital television broadcasting network. This paper addresses the structure of digital television middleware system. The security sub-system is investigated and analyzed. The fundamental principles of construction and operation of the security sub-system accord with Chinese digital television middleware standards are presented. The security sub-system is designed and implemented by using encryption techniques. It has been used in real environment of interactive DTV system .

For the weak of the traditional expert system reasoning ability and intelligent level, the problem of the matching expression of the traditional expert system in the reasoning ability and case is solved by using the particle swarm method in this paper. The basic principle and mathematical expression of the particle swarm expert system are described in detail in this paper. By building reasonable keyword importance model, we decide which case adaptation is the highest according to the overall matching value. Finally, the combination of the case and the realization of t he particle swarm optimization algorithm are carried out.

This paper addresses an end-to-end operation system for digital television (DTV) applications. It is designed to develop, broadcast, download and execute interactive DTV applications. The system is composed of three subsystems. They are application execution system (AES), data broadcast system (DBS) and application development environment (ADE). AES, generally called DTV middleware system, is the software system running in DTV se-top box (STB) to execute and control interactive DTV applications. It isolates DTV applications from STB hardware. DBS is used to multiplex, transmit and broadcast DTV data, as well as manage DTV applications. ADE is a special integrative toolkit designed for developing interactive DTV applications. Application developers can edit, compile, debug and test a DTV application in ADE. Then DBS broadcasts the application through DTV channel. After being downloaded into STB, the application is executed by AES in the STB. Design and implementation of these three subsystems are presented in this paper. The proposed system has been used in real environment of interactive DTV operation