Testability Analysis Research Articles

Testability modeling of aeroengine and analysis optimization method based on improved correlation matrix

This paper aims to optimize the testability analysis method of aero-engines by presenting a testability modeling and an improved correlation matrix method. Because of strong coupling in aero-engines, the traditional testabilitsy modeling method based on graph theory is difficult to accurately express the relationship between faults and test points. Simulation technology can simulate actual work process of system. So this paper launches the research based on simulation model. Firstly, the gas path model is established according to the thermodynamic principle of aero-engines and accuracy of the model is verified. Secondly, common faults of gas path are selected. Affected parameters are obtained after injecting faults into the model, so as to obtain the relationship between faults and test points, that is, the correlation matrix. Then, after going through masses of simulations, it is found that the relationship between faults and test points can be divided into three categories: positive correlation, negative correlation and no correlation. The correlation matrix can be improved by diversifying its elements. During simulation, accuracy of the sensors are not considered. The correlation matrix is optimized with the accuracy of sensors in the gas path as a constraint, so that it is more in line with engineering practice. Finally, four testability characteristics and two testability metrics are defined, and the correlation matrix before and after improvement are analyzed and compared. It is found that the improved correlation matrix can isolate more faults on the premise of reducing test points, which proves the effectiveness of the proposed method.

Static parameter estimation and testability analysis of distribution lines based on multi-source measurement information

The information monitoring of distribution networks usually relies on multiple measurement systems, and the placement and number of measurement configurations are also restricted by objective factors. It is highly likely that insufficient measurements will affect the parameter estimation of distribution lines. To deal with the aforementioned issue, this paper establishes the measurement equations for static parameters of distribution lines and proposes a method of parameter estimation based on the elitist searching approach. Aiming at different measurement conditions, the testability analysis method is introduced to distinguish clear parameters and uncertain parameters of distribution lines. For the constructed line parameter samples, the estimated values of line parameters are calculated with the consideration of multi-source effective measurement data at different time points. Moreover, the indices of relative errors between the sample values and the estimated values are used to judge the testability of static parameters of distribution lines. The case study on IEEE 33 bus distribution system analyzes the application scenarios with and without measurement errors. The simulation results show that the testability analysis method can effectively identify whether the parameters to be estimated are clear or not, and then the estimated values of clear parameters and the bounds of some disturbing uncertain parameters can be estimated accurately to provide certain decision support for engineering applications.

Failure Prevention in DC–DC Converters: Theoretical Approach and Experimental Application on a Zeta Converter

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine.

Testability Evaluation in Time-Variant Circuits: A New Graphical Method

DC–DC converter fault diagnosis, executed via neural networks built by exploiting the information deriving from testability analysis, is the subject of this paper. The networks under consideration are complex valued neural networks (CVNNs), whose fundamental feature is the proper treatment of the phase and the information contained in it. In particular, a multilayer neural network based on multi-valued neurons (MLMVN) is considered. In order to effectively design the network, testability analysis is exploited. Two possible ways for executing this analysis on DC–DC converters are proposed, taking into account the single-fault hypothesis. The theoretical foundations and some applicative examples are presented. Computer programs, based on symbolic analysis techniques, are used for both the testability analysis and the neural network training phase. The obtained results are very satisfactory and demonstrate the optimal performances of the method.

Research on Q-markers of Polygoni Perfoliati Herba based on analytic hierarchy process-entropy weight method and fingerprints

The potential quality markers(Q-markers) of Polygoni Perfoliati Herba were studied based on analytic hierarchy process(AHP)-entropy weight method(EWM), network pharmacology, and spectrum-effect relationship analysis. The AHP-EWM was used for quantitative identification of the Q-markers. To be specific, AHP was applied for the weight analysis of the validity, testability, and specificity of the first-level indexes, and EWM for the analysis of the second-level indexes supported by literature and experimental data. Based on literature and network pharmacology, the validity analysis was to study the component-target-disease-efficacy network, and select the components with the strongest correlation with the efficacy of clearing heat and removing toxin, diuresis and alleviating edema, and relieving cough. For the testability analysis, the high performance liquid chromatography(HPLC) and literature research were used to determine the 10 components in Polygoni Perfoliati Herba, and the fingerprints of Polygoni Perfoliati Herba were established at the same time. The specificity analysis was based on the statistics of the number of plants in which the components existed. Thereby, the 11 compounds: quercetin, oleanolic acid, ellagic acid, gallic acid, kaempferol, rutin, esculetin, quercetin-3-O-glucuronide, ursolic acid, protocatechuic acid, and ferulic acid, were identified as potential Q-markers. The 11 compounds were identified to have high anti-inflammatory activity, indicating that the 11 Q-markers may be the functional material basis. The result in this study is expected to serve as a reference for the quality control of Polygoni Perfoliati Herba.

Testability Analysis Method of Radar Equipment Based on Dependency Model

In order to improve the testability design level of radar equipment, achieve rapid detection and isolation of faults, and reduce the life cycle cost of the system, a testability analysis method of radar equipment based on correlation model is proposed. The basic process of correlation model modeling is introduced. On this basis, the optimization method of test points for fault detection and isolation and the generation method of fault diagnosis strategy are analyzed. The effectiveness of the proposed method is verified by applying it to radar transmitting subsystem.

A testability modeling method based on structure-function-state-test

Aiming at the testability analysis of multi-state system, a testability modeling method based on structure-function-state-test is proposed. Based on the hierarchical structure analysis of the system, taking the function modeling of the structure as the core, the performance, fault and state of the function are described, and the test correlation of the system fault and state is studied. Through the analysis of a typical case, the results show that the model can: 1. Realize the system fault test correlation analysis considering the uncertain factors; 2. Realize the state test correlation analysis of the multi state system; 3. Realize the testability analysis of the multi state system by calculating the fault detection rate, fault isolation rate and state detection rate.

A Novel Algorithm for Hardware Trojan Detection Through Reverse Engineering

Malicious alteration in an IC design is generally referred to as hardware Trojans (HTs). The involvement of multiple entities in the VLSI design cycle has made the process of HT detection very challenging. This article presents a novel method for the detection of HT at the gate level of abstraction. A path retrace algorithm detects the nets added/deleted by an adversary along with its location. The netlists of the genuine circuit (design netlist) and the Trojan-inserted circuit are used by the algorithm to detect the malicious nets in the circuit. This method utilizes testability analysis as the metric for segregating malicious nets in the compromised circuit netlist. Netlist of the fabricated IC, which is obtained through reverse engineering and the design netlist of the original circuit are used to determine the testability parameters, such as controllability and observability of the nets. Based on the variation in testability metrics of a signal in the original circuit, the path retrace algorithm identifies the malicious gates inserted into the original circuit. In addition, the algorithm also helps to isolate the Trojan circuit and comprehend its functional implication on the original design. Using the list of malicious gates and compromised circuit netlist, it is possible to identify the Trojan nets inserted by the adversary. This technique is more effective in detecting functional Trojans as compared to techniques employing reverse engineered images as it is design parameter independent and impervious to noise.

Software for Testability Analysis of Aviation Systems

This paper describes models, methods and software tool for testability analysis of aviation systems. It comprises analytical and programing aspects of calculations of main testability, reliability and availability indices. It presents general description of the software, XML schema of input data and technique of their mapping to the database structure. The procedure for generating the initial data for testability analysis based on the line replaceable units failure modes report is described. Fault tree model for analysis of the built-in test conformity is suggested. Markov models have been created for analyzing reliability and availability, taking into account the features of the built-in test and the specifics of the aircraft operation. An approach to the construction of trends in the operative availability of aviation systems in the inter-maintenance interval is proposed.

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

En begreppsanalys om lidande vid livets slutskede

This study examines the concept of suffering to gain a deeper understanding of the dimensions of suffering at the end of life. Suffering is a central concept in the caring communion at the end of life. To develop an understanding of the concept of suffering, we used semantic concept analysis techniques developed by Koort and analyzed testability using quality content analysis developed by Graneheim and Lundman. The materials analyzed included 14 dictionaries and an autobiography. The concept analysis of suffering revealed three dimensions related to end-of-life care: tolerance, endurance, and agony. These dimensions were continuously referred to throughout the testability analysis. The dimensions can give a deeper understanding for the patients’ total suffering in end-of-life care and their relatives.

Research on Testability Design and Evaluation Method for Ship Electromechanical Equipment

As a relatively large amount of electromechanical equipment was installed in the ship systems, it was difficult to position and eliminate the faults of the M&E equipment if any. Hence, there was a need to improve the fault detection and isolation capabilities of the equipment depending on testability design and to develop the corresponding fault diagnosis system when necessary, thus improving the overall availability of the equipment and ensuring that the ship could successfully complete the missions. In view of the above requirements, this paper, taking the most representative M&E equipment - pump set on the ship as a research object, gave the testability design process of the M&E equipment - pump set for product development, illustrated the equipment testability design methods from 4 aspects (namely determination of equipment testability and fault diagnosis system requirements, testability analysis & design, comprehensive fault diagnosis system design and testability verification process design), specified the testability design & monitoring object selection principles and fault characteristic determination methods, and finally provided the testability design evaluation methods for the M&E equipment for verifying whether the testability requirements were met.

Calculation of probabilistic testability measures for digital circuits with Structurally Synthesized BDDs

A method is proposed for probabilistic testability analysis of digital circuits focusing on calculating the probabilistic controllability measures in terms of signal probabilities with the goal of assessment of pseudorandom test quality in digital circuits. The structure of the circuit is modeled as a macro-level network, where macros denote Fan-out-Free Regions (FFRs) of the circuit, which are represented as Structurally Synthesized BDDs (SSBDDs). SSBDD based representation allows signal probability calculation with higher speed and accuracy than using gate-level calculation approach. The proposed method is based on tracing true paths in SSBDDs, which avoids errors caused by signals' correlation and possible redundancy in the circuit, that is not possible in gate-by-gate probability calculation. A method is proposed for proving redundancy of faults, which is an extension of the same idea of SSBDD path tracing used for probability calculation. Experimental results show higher accuracy and considerable speed-up of probabilistic analysis using the proposed new macro-level approach, compared to gate-level calculation.

Sensitivity analysis of testability parameters for secure IC design

Insertion of malicious circuits commonly known as Hardware Trojans into an original integrated circuit (IC) design to alter the functionality has been a major concern in recent years. As a result, over the years multiple techniques have been suggested by researchers to combat these malicious threats. Hard to test nets in any logic circuit are the most vulnerable to insertion of Hardware Trojans. Testability analysis is the process of identification of these hard to test nets in a logic circuit. Testability analysis is achieved through the testability metrics namely controllability and observability. Testability metrics can be used as a yardstick in devising efficient Hardware Trojan detection methods. The crux of this study is a novel method for identification of susceptible nets that are prone to Hardware Trojan insertions in a logic circuit. The study also presents a comprehensive analysis of the impact on testability parameters as a result of Hardware Trojans in the identified susceptible nets. The method utilises the testability parameters of nets to define threshold values for isolating susceptible nets in a design. The study details out the impact of the number of trigger inputs as well as the distribution of trigger nets on the testability metrics of digital circuits.

A Novel Method for Detecting Parametric Faults in Analog Circuits using Fuzzy Logic

The demand for testability analysis of analog circuits has been increased in recent years. The fault detection and fault classification method is important in detecting the parametric faults of the circuit. In this paper, Simulation Before Test (SBT) is considered as a basic mechanism for detecting the parametric faults.. The circuit Under Test (CUT) used is Sallen-Key bandpass filter. Transfer function of the CUT is used for fault detection by locating the poles and Zeros of the transfer function. Fuzzy logic is used for fault classification.

Continuous Time Bayesian Networks in Prognosis and Health Management of Centrifugal Pumps

This paper presents a novel method for performing risk-based prognosis and health management (rPHM) on centrifugal pumps. We present the rPHM framework and apply common modeling tools used in reliability and testability analysis---dependency (D) matrices and fault tree analysis---as a basis for constructing an underlying predictive model. We then introduce the mathematics of the Continuous Time Bayesian Network (CTBN), which is a probabilistic graphical model based on a factored Markov process that is designed to capture system evolution through time, and we explain how to apply a CTBN derived from D-matrices and fault trees to consider the impact of a set of faults common to centrifugal pumps on emerging hazards in the pump system. We demonstrate the utility of using CTBNs for rPHM analysis with two experiments showing the descriptive power of our modeling approach.

Modeling and Diagnosis of Joints in High Voltage Electrical Transmission Lines

In this paper a new method is developed and described, aimed at the modeling and diagnosis of the joints connecting the ends of two cables on a high voltage electricity pylon. Identifying the anomalous joint behaviour through the line frequency response analysis is the first objective of the work. For this reason, it is necessary to model the whole overhead line with a lumped circuit and studying the joint electrical parameter variation. The problem is approached in multiple steps: modelling, testability analysis of the model, optimal frequency selection, identification of the possible failures. Some simulated cases are included in order to show the potentialities of the method.

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

Design and Measurement Requirements for Short Flow Test Arrays to Characterize Emerging Memories

Emerging non-volatile memories are becoming increasingly attractive for embedded and storage-class applications. Among the development challenges of Back-End integrated memory cells are long learning cycles and high wafer cost. We propose a short-flow based approach for characterization of Memory Arrays using a Cross-Point Array structure and highly parallel Parametric Test. A detailed analysis of design requirements and testability, including inverse circuit simulation, confirms feasibility of the approach to reduce Turn-Around Time and development costs.

Using homing, synchronizing and distinguishing input sequences for the analysis of reversible finite state machines

A digital device is called reversible if it realizes a reversible mapping, i.e., the one for which there exist a unique inverse. The ?eld of reversible computing is devoted to studying all aspects of using and designing reversible devices. During last 15 years this ?eld has been developing very intensively due to its applications in quantum computing, nanotechnology and reducing power consumption of digital devices. We present an analysis of the Reversible Finite State Machines (RFSM) with respect to three well known sequences used in the testability analysis of the classical Finite State Machines (FSM). The homing, distinguishing and synchronizing sequences are applied to two types of reversible FSMs: the converging FSM (CRFSM) and the non-converging FSM (NCRFSM) and the effect is studied and analyzed. We show that while only certain classical FSMs possess all three sequences, CRFSMs and NCRF-SMs have properties allowing to directly determine what type of sequences these machines possess.