Improved cost effectiveness for new satellite systems

Concepts of reliability, life and earning power are applied in a new way to measure the cost effectiveness and depreciation of satellite systems in view of requirement versus capability.The operational reliability of Telesat's present satellite systems is evaluated against the early assumptions of constant failure rates. New realistic reliability wearout models are developed and result in reliability predictions that correlate well with operational data.Optimization and selection of cost effective configurations for new increased capability satellite systems are achieved by a series of tradeoffs using the new reliability wearout models. The new systems show a substantial improvement in the level of cost effectiveness optimization over a substantially longer life in comparison with the present systems. The depreciation of the new systems is also shown to be more uniformly distributed over longer life.     

A system approach to reliability and life-cycle cost of processsafety-systems

An analytic method, PDS, allows the designer to assess the cost effectiveness of computer-based process safety-systems based on a quantification of reliability and life-cycle cost. Using PDS in early system design, configurations and operating philosophies can be identified in which the reliability of field devices and logic control units is balanced from a safety and an economic point of view. When quantifying reliability, the effects are included of fault-tolerant and fault-removal techniques, and of failures due to environmental stresses and failures initiated by humans during engineering and operation. A failure taxonomy allows the analyst to treat hardware failures, human failures, and software failures of automatic systems in an integrated manner. The main benefit of this taxonomy is the direct relationship between failure cause and the means used to improve safety-system performance     

Diagnostic tolerance for missing sensor data

For practical automated diagnostic systems to continue functioning after failure, they must not only be able to diagnose sensor failures but also be able to tolerate the absence of data from the faulty sensors. We show that conventional (associational) diagnostic methods will have combinatoric problems when trying to isolate faulty sensors, even if they adequately diagnose other components. Moreover, attempts to extend the operation of diagnostic capability past sensor failure will necessarily compound those difficulties. By contrast, the algorithmic methods of model-based reasoning have no special problems diagnosing faulty sensors and can operate gracefully when sensor data is missing.     

Queuing Models for Estimating Aircraft Fleet Availability

The availability of a small fleet of aircraft in a flying-base, repair-depot combination is modeled and studied. First, a deterministic flow model relates parameters of interest and represents the state-of-the art in the planning of such systems. Second, a cyclic queue model shows the effect of the principal uncertainties in operation and repair and shows the consequent decrease in the availability of aircraft at the flying-base. Several options such as increasing fleet size, investments in additional repair facilities, or building reliability and maintainability into the individual aircraft during its life-cycle are open for increasing the availability. A life-cycle cost criterion brings out some of these features. Numerical results confirm Rose's prediction that there exists a minimal cost combination of end products and repair-depot capability to achieve a prescribed operational availability.     

Reliability Analysis of Systems Comprised of Units with Arbitrary Repair-Time Distributions

This work demonstrates the feasibility of reliability modeling of systems with repair capability using a semi-Markov process. A two-unit system with exponential failure times but general repair times is studied. Formulas for state-transition probabilities, waiting-time distribution functions, and mean time in each state are developed. These quantities are expressed in terms of the Laplace transform of repair time distribution functions. Once these quantities are known, mean time to system failure and system availability, as well as other system parameters, can be found using matrix manipulations. In addition, time-dependent results may be obtained. A numerical example varying the parameter in a repair-time law is presented. The formulas developed can be extended to larger systems with repair capability for only one unit at a time and exponential failure times.     

Reliability Investment and Life-Cycle Cost

The reliability of avionic equipment profoundly influences life-cycle cost; the level of reliability attained largely depends upon the investment in reliability programs during development. As more investment is made in reliability improvement, some cost elements increase and others decrease. These opposing cost trends yield a unique minimum life-cycle cost (LCC). In order to find the level of investment in a reliability improvement program that minimizes LCC, the Reliability Investment Optimization (RIO) model has been developed. It identifies, for a particular avionic system, the level of reliability investment that minimizes the LCC of the equipment. This model employs a reliability-growth relationship based on the Duane model. The RIO model uses this reliability growth pattern to compute LCC as a function of MTBF (mean time between failures) where LCC comprises: 1) research, development, test and evaluation (RDT&E), 2) procurement, and, 3) operations and support (O&S). The RIO model uses summary level data that are appropriate for the timeframe of its most advantageous use, i.e., prior to detail design of the system. The degree of accuracy for the input parameters need not be high because results are not very sensitive to data accuracy. The model's results thus are quite stable. The RIO model was designed with avionic systems in mind. However, the model applies to a wider range of systems. Certain assumptions should be particularly scrutinized in extending usage beyond avionics, e.g., Poisson demand assumption versus a wearout failure pattern (failure rate increases over time), scheduled maintenance, and LCC element breakdown.     

Fault Diagnosis with Imperfect Tests

This paper derives a minimum s-expected cost sequence of built-in-tests (BITs) which will partition modular equipment into mutually exclusive groups of modules. After a fault in the equipment, one of these groups will be identified by a BIT diagnostic subsystem as the group which contains a faulty module. The BITs are imperfect in the sense only that they might not detect all of the possible faults in the equipment; they are perfect in the sense that fault indications are never false. The proportion of faults detectable by each BIT is known. Both the cost of a BIT and the probability that a BIT will pass or fail are functions of which modules are tested. A recursive algorithm is developed which determines a sequence of BITs with a minimum s-expected life-cycle cost. The recursive algorithm is applied to a 4-element numerical example. The algorithm has neither been proved nor implemented for a computer.     

Markov Approach to Finding Failure Times of Repairable Systems

In analyzing repairable systems it is often necessary to determine such parameters as availability, mean cycle time, and mean time to first failure. These and other failure time measures are defined, and methods of calculating them using a Markov approach are developed. Although conceptually simple, these methods are often not practically feasible because of the large number of possible system states. Various special techniques such as lumping states or decomposing the system into independent subsystems are discussed. These techniques, if applicable, can simplify the analysis considerably.     

Automatic Generation of Hybrid Hw/Sw Applications by means of COTS Component Integration

The great advances achieved in the field of electronic engineering have given rise to new hybrid systems which combine the higher flexibility of software (Sw) with the better performance of hardware (Hw). In this paper we present a novel approach to the design and implementation of this kind of hybrid systems which covers their whole development life-cycle. This proposal integrates two of the currently most popular software development paradigms: component-based software engineering and automatic software generation. Although this paper presents the results of applying this new approach to the specific domain of Visual Information Processing Systems (VIPS), it can be easily extended to cover different domains such as control applications or telecommunication systems. As a part of this work a new visual programming tool called IP-CoDER has been developed which allows incrementally building new VIPS from the design and partitioning of functional prototypes to the automatic generation of the final Hw/Sw hybrid system.     

Using generalized additive models for construction of nonlinear classifiers in computer-aided diagnosis systems.

Several investigators have pointed out the possibility of using computer-aided diagnosis (CAD) schemes, as second readers, to help radiologists in the interpretation of images. One of the most important aspects to be considered when the diagnostic imaging systems are analyzed is the evaluation of their diagnostic performance. To perform this task, receiver operating characteristic curves are the method of choice. An important step in nearly all CAD systems is the reduction of false positives, as well as the classification of lesions, using different algorithms, such as neural networks or feature analysis, and several statistical methods. A statistical model more often employed is linear discriminant analysis (LDA). However, LDA implies several limitations in the type of variables that it can analyze. In this work, we have developed a novel approach, based on generalized additive models (GAMs), as an alternative to LDA, which can deal with a broad variety of variables, improving the results produced by using the LDA model. As an application, we have used GAM techniques for reducing the number of false detections in a computerized method to detect clustered microcalcifications, and we have compared this with the results obtained when LDA was applied. Employing LDA, the system achieved a sensitivity of 80.52% at a false-positive rate of 1.90 false detections per image. With the GAM, the sensitivity increased to 83.12% and 1.46 false positives per image.     

Reliability growth modeling for in-service electronic systems considering latent failure modes

A latent failure mode is a type of failure that may not occur until the system has operated in the field for a certain period of time. Predicting latent failures is often difficult, but it has a great importance for reliability management in terms of system maintenance and warranty services. This paper proposes a stochastic model to predict the reliability growth for field or in-service electronic systems considering latent failures. The proposed model can be applied to electronics industries where extended in-house reliability testing cannot be implemented due to the tight design schedule. Based on the new method, the product management can proactively implement corrective actions against key failure modes using relevant engineering resources. A discussion between the effectiveness of corrective actions and the associated cost is also provided. Finally, field failure data collected from a fleet of automatic test equipment are used to demonstrate the applicability and performance of the model.     

Failure analysis of contact probe pins for SnPb and Sn applications

A study has been conducted to investigate the failure mechanism of pogo pin-type probe contacts. Probe pins are used for electrical test of microelectronic components in manufacturing. A false rejection of parts due to high probe contact resistance results in a penalty in cost and yield. The probe pin contact bears distinctive characteristics of failure compared to the conventional contact systems such as mechanical switches and interconnects. Moreover, the transition to Pb-free component leads demands understanding of different probe failure mechanisms between a SnPb and Sn surface. The objective of this study is to understand this unique failure mechanism and the effect of lead coating metals on probe pin life. This has not been clearly elucidated to date in spite of its significant impact on yield and cost of electronic package manufacturing. A simulated probe tester with 3-axes actuation capability was devised to mimic the actual test process. The force required to penetrate the surface oxide layer and develop electrical contact was measured. Contact resistance history revealed that probe pins mating to Sn surfaces failed earlier than pins used on SnPb surfaces. Through periodic inspection of probe pins using microprobe/EDS as a function of probe actuations, the general root cause of probe pin failure was found to be probe pin tip wear out associated with the Sn oxide growth on its surface. The matte Sn surface wears the probe pin more than SnPb due to the rough and abrasive nature of the matte Sn surface.     

An architecture for the development of real-time fault diagnosis systems using model-based reasoning

This article presents an architecture for implementing real-time telemetry-based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for off-line entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run-time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed off-line by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.     

Environmental stress screening strategies for complex systems: A 3-level mixed distribution model

Environmental Stress Screening (ESS) is employed to reduce, if not eliminate, the occurrence of early field failures. In this paper, a three level ESS model is presented for a complex electronic system. Screening is performed at the component, board and system level. Components are screened for a specified duration before being assembled into printed circuit boards (PCBs). Defects introduced during the assembly of the PCBs are screened at the board level, while defects introduced during final assembly are screened at the system level. Components and connections are assumed to come from good and substandard populations and their times-to-failure distributions are modeled by mixed distributions. Mixed exponential distributions are used to model component timesto-failure and mixed Weibull distributions are used to model the times-to failure for board and system level connections. The mixed Weibull distributions are used to model wear-out characteristics at the board and system level. Optimal screen durations in the presence of wear-out are obtained by minimizing the systems life-cycle cost. ESS is shown to be a cost effective strategy when properly implemented. The optimal screening strategies are shown to be relatively robust to the system warranty period.     

集成压电健康监测扫查系统的研制及其应用

结构健康监测能增强结构的功能,提高其安全性和可靠性,降低维护费用,延长其使用寿命。基于压电元件和兰姆(Lamb)波主动诊断技术的结构健康监测法是一种典型的、行之有效的热点研究方法。目前,常规的结构健康监测系统主要在实验室中使用,因此大多采用独立仪器搭建,系统复杂,质量大,连线多,使用不方便,不适合实际工程应用的需要。因此,该文研究了一套多通道集成压电结构健康监测扫查系统,并通过实际工程应用对这套系统的功能进行验证研究。     

Reliability analysis of non-maintained parallel systems subject to hardware failure and human error

This paper presents four newly developed Markov models to evaluate reliability and mean time to failure of non-maintained parallel systems with hardware failure and human error. The first three models represent two-unit, three-unit and four-unit non-maintained parallel systems, respectively, whereas the fourth model generalises the analysis to a non-maintained n-unit parallel system. System state probability equations and mean time to failure expressions are developed for all four models.     

测试不可靠条件下基于量子进化算法的测试优化选择

测试优化选择是复杂电子系统测试性设计中的一个重要问题.首先从测试容差的角度分析了测试发生漏检和虚警的原因,在此基础上建立了测试不可靠条件下一种新的测试选择模型,模型以测试代价、漏检代价和虚警代价之和最小为优化目标,以故障检测率和故障隔离率为约束条件;然后提出一种改进的量子进化算法对模型求解,该算法通过改进一种已有可靠测试选择算法而成,包括种群初始化、适应度计算和种群的进化策略.最后通过两个仿真实例验证了求解算法及模型的有效性和优越性.     

Fault Diagnosis of Nonlinear Analog Circuits Using Neural Networks with Wavelet and Fourier Transforms as Preprocessors

A neural-network based analog fault diagnostic system is developed for nonlinear circuits. This system uses wavelet and Fourier transforms, normalization and principal component analysis as preprocessors to extract an optimal number of features from the circuit node voltages. These features are then used to train a neural network to diagnose soft and hard faulty components in nonlinear circuits. Our neural network architecture has as many outputs as there are fault classes where these outputs estimate the probabilities that input features belong to different fault classes. Application of this system to two sample circuits using SPICE simulations shows its capability to correctly classify soft and hard faulty components in 95% of the test data. The accuracy of our proposed system on test data to diagnose a circuit as faulty or fault-free, without identifying the fault classes, is 99%. Because of poor diagnostic accuracy of backpropagation neural networks reported in the literature (Yu et al., Electron. Lett., Vol. 30, 1994), it has been suggested that such an architecture is not suitable for analog fault diagnosis (Yang et al., IEEE Trans. on CAD, Vol. 19, 2000). The results of the work presented here clearly do not support this claim and indicate this architecture can provide a robust fault diagnostic system.     

Blind Deconvolution Denoising for Helicopter Vibration Signals

Critical aircraft assets are required to be available when needed, while exhibiting attributes of reliability, robustness, and high confidence under a variety of flight regimes, and maintained on the basis of their current condition rather than on the basis of scheduled maintenance practices. New and innovative technologies must be developed and implemented to address these concerns. Condition-based maintenance requires that the health of critical components/systems be monitored and diagnostic/prognostic strategies be developed to detect and identify incipient failures and predict the failing component's remaining useful life. Typically, vibration and other key indicators onboard an aircraft are severely corrupted by noise, thus curtailing the ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution denoising scheme that employs a vibration model in the frequency domain and attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes are defined and data from a helicopter are used to demonstrate the effectiveness of the proposed approach.     

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.