Diagnostic Engine Research Articles

A Broad Set of Different Llama Antibodies Specific for a 16 kDa Heat Shock Protein of Mycobacterium tuberculosis

BackgroundRecombinant antibodies are powerful tools in engineering of novel diagnostics. Due to the small size and stable nature of llama antibody domains selected antibodies can serve as a detection reagent in multiplexed and sensitive assays for M. tuberculosis.Methodology/Principal FindingsAntibodies for Mycobacterium tuberculosis (M. tb) recognition were raised in Alpaca, and, by phage display, recombinant variable domains of heavy-chain antibodies (VHH) binding to M. tuberculosis antigens were isolated. Two phage display selection strategies were followed: one direct selection using semi-purified protein antigen, and a depletion strategy with lysates, aiming to avoid cross-reaction to other mycobacteria. Both panning methods selected a set of binders with widely differing complementarity determining regions. Selected recombinant VHHs were produced in E. coli and shown to bind immobilized lysate in direct Enzymelinked Immunosorbent Assay (ELISA) tests and soluble antigen by surface plasmon resonance (SPR) analysis. All tested VHHs were specific for tuberculosis-causing mycobacteria (M. tuberculosis, M. bovis) and exclusively recognized an immunodominant 16 kDa heat shock protein (hsp). The highest affinity VHH had a dissociation constant (KD) of 4×10−10 M.Conclusions/SignificanceA broad set of different llama antibodies specific for 16 kDa heat shock protein of M. tuberculosis is available. This protein is highly stable and abundant in M. tuberculosis. The VHH that detect this protein are applied in a robust SPR sensor for identification of tuberculosis-causing mycobacteria.

A new strategy for automotive off-board diagnosis based on a meta-heuristic engine

The automotive industries lack of solutions for accurately, comprehensively and efficiently fault localization. However, such services in the after-sales networks are crucial to the brand value of automotive manufacturer and for client satisfaction. In this paper, a new approach for the off-board diagnosis is presented, with significant improvements compared to the current technologies usually based on inference rules. A more robust approach that allows, per the additions of functional modules, to enhance traditional computer aided diagnostic systems towards a global diagnostic engine reasoning on different sources of knowledge with their uncertainties. Once the design of a new vehicle has begun, information like the dependencies between the components could be re-used for the models dedicated to the diagnosis task. Moreover, the economic pressure leads to a high degree of innovation with a massive use of electronics in safety, comfort and entertainment ( OCC'M Software GmbH, 2010). This dramatically increases the amount of data to manage for the testing of E.C.U. (Electronic Control Unit) functionalities. The complexity of the subsystems leads to breakdowns that need qualitative symptom description for the fault localization. Finally, a feedback engine automatically completes the expensive models for the diagnosis and returns critical dysfunctions to the design department. In this paper, we present our research on a new diagnosis strategy for complex mechatronics systems. It encompasses the needs and requirements of automotive manufacturer. The results are presented with data obtained from low, middle and luxury class vehicles. They demonstrate the performance in real field conditions of our approach. They are based on the interpretation of observations, the fault localization and isolation, the evaluation of feedbacks for model auto-completion. The novelty in this approach is based on the reasoning of different sources of knowledge (construction and design knowledge, expert knowledge, return of experiences) which leads to an efficient diagnosis. The approach approximates the optimal path from the observations toward the fault isolation with the help of a meta-heuristic engine. These experiences show the potential of our proposed approach for the automotive off-board diagnosis task.

Performance Model “Zooming” for In-Depth Component Fault Diagnosis

A method giving the possibility for a more detailed gas path component fault diagnosis by exploiting the “zooming” feature of current performance modeling techniques is presented. A diagnostic engine performance model is the main tool that points to the faulty engine component. A diagnostic component model is then used to identify the fault. The method is demonstrated on the case of compressor faults. A 1D model based on the “stage stacking” approach is used to “zoom” into the compressors, supporting a 0D engine model. A first level diagnosis determines the deviation of overall compressor performance parameters while zooming calculations allow a localization of the faulty stages of a multistage compressor. The possibility to derive more detailed information with no additional measurement data is established by the incorporation of empirical knowledge on the type of faults that are usually encountered in practice. Although the approach is based on known individual diagnostic methods, it is demonstrated that the integrated formulation provides not only higher effectiveness but also additional fault identification capabilities.

Search State Compatibility Based Incremental Learning Framework and Output Deviation Based X-filling for Diagnostic Test Generation

Silicon Diagnosis is the process of locating potential defect sites (candidates) in a defective chip. These candidates are then used as an aid during physical failure analysis. It is desired that the cardinality of the candidate set returned by silicon diagnosis be as small as possible. To this end, effective test patterns that can distinguish as many fault-pairs in the candidate set are critical. Generation of such diagnostic patterns is referred to as Automatic Diagnostic Test Generation (ADTG). In this paper, we propose an aggressive and efficient learning framework for such a diagnostic test generation engine. It allows us to identify and prune non-trivial redundant search states thereby allowing to easily solve hard to distinguish or hard to prove equivalent fault-pairs. Further, we propose an incremental flow for ADTG, where the information learned during detection-oriented test generation is passed to and incrementally used by ADTG. Finally, we propose an interesting output deviation based X-filling of detection test patterns with the objective of enhancing test set's diagnostic ability. Experimental results on full-scan versions of ISCAS89/ITC99 circuits indicate that our incremental learning framework achieves up to 2× speed-up and/or resolves more initially unresolved fault-pairs for most circuits. Also, results indicate that the proposed X-filling method has the potential to distinguish more fault-pairs than the random X-filling method.

Plasma Diagnostics in View of ITER

The ITER environment imposes many challenges for the various diagnostic systems. At the one hand diagnostic functionalities are required that go well beyond those at present devices. This is because there is a need to actively control (the profiles of) multiple plasma parameters, implying that measurement systems should be accurate and reliable. At the other hand the application of diagnostics at ITER is strongly hampered by constraints arising from the relatively harsh environmental conditions that give rise to phenomena that are new to the diagnostic designs. The nuclear environment puts stringent demands on the engineering and robustness of diagnostics, while the long pulse lengths require high stability of all systems. This paper will present an overview of the diagnostics for ITER with an additional glance in the further future.

Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering

A prerequisite to widespread deployment of condition-based maintenance (CBM) systems in industry is autonomous yet effective diagnostics and prognostics algorithms. The concept of ‘autonomy’ in the context of diagnostics and prognostics is usually based on unsupervised clustering techniques. This paper employs an unsupervised competitive learning algorithm to perform hidden Markov model (HMM) based clustering of multivariate temporal observation sequences derived from sensor signal(s). This method improves autonomy of the diagnostic engine over the more traditional classifier based diagnostics models. Classifier models, such as the model presented by Baruah and Chinnam [Baruah, P. and Chinnam, R.B., 2005. HMM for diagnostics and prognostics in machining processes. International Journal of Production Research, 43 (6), 1275–1293] employ ‘labelled’ feature vectors for supervised model building and subsequent health-state classification during condition monitoring. Improving the autonomy of the diagnostics model also improves the autonomy of the prognostics module that often builds upon information extracted through the diagnostics module. We have validated the proposed methods on a physical test-bed that involved monitoring drill-bits on a CNC machine outfitted with thrust-force and torque sensors. Experimental results demonstrate the ability of this method to estimate on-line the remaining-useful-life of a drill-bit with significant accuracy.

Fault Diagnosis for a Booster Pump Unit

Condition monitoring and diagnostic engineering is a course that is well recognized by academics. It is offered to students across the globe who want exposure to instrumentation, data acquisition and processing. This paper introduces the technique of condition monitoring to budding engineers, from its conception to the present state of development, through a case study. An experimental investigation is highlighted in which the vibration condition of booster pump unit is monitored; the unit is a part of boiler feed pump train of a large thermal power plant. The booster pump is driven by a 3500 kW, 1440 rpm motor. The motor, along with the booster pump, is supported by four bearings. Tri-axial measurements were made at the bearing supports for 12 months. Root mean square values of displacement and velocity were measured along the horizontal, vertical and axial directions. The experimental data are plotted on a time domain for graphical analysis. Based on the experimental data, faults are diagnosed using ISO standards and causes are predicted. It is observed that the front and rear bearings of the booster pump experienced excess vibration. The work is concluded by way of suggesting remedial measures to ensure the vibration intensity at the said points falls within safe limits.

Incremental state based diagnosis

In this paper we present an incremental diagnostic engine: IMBDE. IMBDE utilises qualitative models, and combines an extended version of Nayak and Williams’ ITMS (based on root antecedents) with an incremental version of Reiter’s Hitting Set Algorithm to perform incremental state based diagnosis on dynamic process systems. The approach is demonstrated to work by application to an industrial process – an ammonia washer system.

Design and engineering of optical diagnostics for COMPASS

The paper generally outlines a design of new multichannel optical diagnostics for the COMPASS (COMPact ASSembly) tokamak. The final design of a port plug for a multispectral diagnostic tool is introduced and its evolution taking into consideration spatial and other technical restrictions is reviewed. A similar solution of both focusing optics and optical signal diversion will be used for a new Thomson scattering (TS) system, which is specified in the second part of the article. Main parameters of the TS system, such as laser energy and regions of observation, were determined with respect to both electron temperature and density profiles and required ultrahigh spatial resolution at the plasma edge.

A Fault Diagnosis Toolbox Applying Classification and Inference Method

This paper presents a MatLab/SIMULINK based toolbox for fault diagnosis. It has been witnessed that while the fault detection methods must be tailored specifically to the process that is to be supervised, the fault diagnosis methods on the other hand are very similar in most applications. Therefore, a MatLab/SIMULINK-based toolbox was developed by the authors which shall be presented in this paper and is available for download. The software is designed such that it can be used with the Real-Time Workshop and can thus be compiled and downloaded to a wide range of rapid control prototyping system. Depending on the type and availability of a-priori knowledge, one can either employ classification or inference methods. Both approaches are supported by development environment. Classification is used whenever there are experimental data available which describe the influence of the faults on the symptoms. The available implementations encompass the Bayes classifier, the k-nearest neighbor and the polynomial classifier. Inference methods are used, whenever rules or expert knowledge describing the influence of the fault on the symptoms are available. In the paper, a Fuzzy-Logic based inference engine is presented, where the symptoms are first fuzzified to account for the uncertainty in the reaction of residuals. Then, the individual symptoms are combined using Fuzzy-Logic AND and OR operators respectively. The mapping of the fuzzy outputs to the diagnosed fault is accomplished by determining the maximum fault possibility among all fault possibilities. The different diagnostic engines have already successfully been applied to a wide range of prototype fault management realizations at the institute and have proven very capable.

BioMEMS in Diagnostics: A Review and Recent Developments

In recent years, the interest in developing micro-electro-mechanical systems (MEMS) for biological and medical applications (BioMEMS) has drastically increased due to the impressive developments in the field of molecular biology, medicine, biochemical and material sciences as well as in microsystems technology. BioMEMS are extensively applied in diagnostic research, drug delivery, therapeutics, tissue engineering, biosensors and lab-on -a-chip systems. This paper focuses on BioMEMS applied in diagnostics such as the study of DNA, cells, proteins and viruses in lab as well as in point-of-care conditions and describes recent achievements in this important field. The future directions and prospects are also discussed. Miniature diagnostic devices are considered to dramatically increase the sensitivity of several test routines, and therefore the reliability of the diagnosis improves while the costs are reduced. Additionally, a step towards point-of-care diagnostics is achieved by BioMEMS development since the possibility of developing a system that integrates an entire test system in an affordable hand-held device is increased.

Chapter 1: Plasma Measurements: An Overview of Requirements and Status

This paper introduces this special issue on plasma diagnostics for magnetic fusion research. Its primary purpose is to relate the measurements of plasma parameters to the physics challenges to be faced on operating and planned devices and also to identify the diagnostic techniques that are used to make these measurements. The specific physics involved in the application of the techniques will be addressed in subsequent chapters. This chapter is biased toward measurements for tokamaks because of their proximity to the burning plasma frontier, and to setting the scene for the development work associated with ITER. Hence, there is some emphasis on measurements for alpha-physics studies and the need for plasma measurements as input to actuators to control the plasma, both to optimize the device performance and to protect the surrounding material. The very different approach to the engineering of diagnostics for a burning plasma is considered, emphasizing the needs for new calibration ideas, reliability and hardness against, and compatibility with, radiation. New ideas take a long time to be converted into “workhorse” sophisticated diagnostics so that investment in new developments is essential for ITER, particularly for the measurement of alpha particles.

Editorial Perspectives - Chapter 13: Challenges in Fusion Diagnostic Development: A Virtual Tour of ITER

The physics basis for almost all the diagnostics planned for ITER is reasonably well in hand. However, the radiation environment near the ITER plasma creates unique challenges for diagnostic engineering. To illustrate this, we take a virtual tour of the ITER complex, beginning in the control room and diagnostic hall, where familiar components are configured much as they are in existing facilities. As we move more closely to the plasma, however, crossing into different zones for access and hazard confinement, the nuclear and plasma edge environment drives diagnostic designs in new directions, with new uncertainties. In each region, anticipated advances in supporting technology will be described, and new strategies for diagnostic implementation will be explained. The need for new standards of reliability will be highlighted, due to the difficulty of robotic maintenance and repair.

The Problem of Test Latency in Machine Diagnosis

The impact of delayed sensor alarm data upon a diagnostic inference engine appears not to be well appreciated. In this paper, we illustrate the effect of sensor latency, and we propose an inference approach to obviate it.

Latest Developments in Gasoline Auto-Ignition Modelling Applied to an Optical CAI (Tm) Engine

The accuracy of predictive engine modelling for Compression Ignition (CI) combustion modes is heavily depending on the quality of the description of the auto-ignition process. The Controlled Auto- Ignition (CAITM ) combustion process is a CI combustion mode using gasoline as fuel. Its modelling requires to account for the chemical characteristics of the fuel in low temperature, low equivalence ratio operating conditions. Existing models based on correlations do not allow to cover the whole range of thermodynamic conditions encountered in those engines and the effect of local inhomogeneities on the auto-ignition occurrence. A dedicated auto-ignition model derived from detailed chemical kinetics calculations for a wide range of burnt gas dilution ratios and octane numbers is presented in this paper. Depending on local thermodynamic conditions, 3D CFD auto-ignition parameters are interpolated in a database describing low temperature effects and progress variable reaction rates for Primary Reference Fuels (PRF). The model validation is performed by comparing 3D CFD calculations with detailed chemical kinetics results for homogeneous configurations and rapid compression machines. To highlight the basic thermal and chemical processes responsible for the occurrence of auto-ignition in a CAITM engine, a reference operating point for an optical diagnostic engine is simulated. The assessment of the quality of the model prediction relies not only on global data, such as mean in-cylinder pressure, but also on the comparison of local volumetric reaction rates with the direct visualisation of light emitted during engine operation.

Design of lost alpha particle diagnostics for JET

In a future magnetic fusion reactor alpha particles will be utlilized for plasma heating. In order to achieve a high efficiency of this process, the aim has to be a good confinement of alpha particles. Therefore, direct measurement of alpha particle losses is of particular interest. Two diagnostics are being prepared for the JET Tokamak that are targeting on exactly this subject: a scintillator probe and a set of Faraday cups [A. Lioure, A. Kaye, A. Murari, J. Sanchez, T. Todd, C. Damiani, J. Pamela, JET-EFDA Contributors, The JET-Enhanced Performance Program: more heating power and diagnostic capabilities in preparation for ITER, Fusion Eng. Des. 74 (2005) 141] . These systems are capable of measuring ICRH tail ions and charged fusion products. The scintillator probe aims to allow the detection of particles with a pitch angle between 30° and 86° (5% resolution) and a gyroradius between 20 and 140 mm (15% resolution). The Faraday cup array will detect the current of fast ions at multiple poloidal locations, with a dynamic range of 1 nA/cm 2 to 100 μA/cm 2 at a temporal resolution of 1 ms. For 3.5 MeV α-particles the energy binning of the foil detector will be 15–50% of the full energy depending on the geometry of the individual collector. The experience in operating both diagnostics in a high temperature and high radiation environment will give valuable information in preparation for the design of similar diagnostics for future fusion devices. This paper covers the design and engineering of both diagnostics together with their envisaged performance.

A model-based approach to robot fault diagnosis

This paper presents a model-based approach to online robotic fault diagnosis: First Priority Diagnostic Engine (FPDE). The first principle of FPDE is that a robot is assumed to work well as long as its key variables are within an acceptable range. FPDE consists of four modules: the bounds generator, interval filter, component-based fault reasoner (core of FPDE) and fault reaction. The bounds generator calculates bounds of robot parameters based on interval computation and manufacturing standards. The interval filter provides characteristic values in each predetermined interval to denote corresponding faults. The core of FPDE carries out a two-stage diagnostic process: first it detects whether a robot is faulty by checking the relevant parameters of its end-effector, if a fault is detected it then narrows down the fault at the component level. FPDE can identify single and multiple faults by the introduction of characteristic values. Fault reaction provides an interface to invoke emergency operation or tolerant control, even possibly system reconfiguration. The paper ends with a presentation of simulation results and discussion of a case study.

Integrated views of e-government website usability: perspectives from users and web diagnostic tools

It has now become evident that information and communication technologies (ICTs) are pertinent for economic prosperity, which has led to governments around the globe to offer online government products and services. For this purpose, websites offering information from governments to citizens and interaction between governments and citizens using static web pages and web portals are considered to be essential. This paper reports the results of an evaluative study of a cross-section of the e-government portals using a common set of performance metrics and web diagnostic engines and empirical evidence gathered from a diverse range of respondents. Results show that not only are there wide variations in the spectrum of information and services provided by these portals, but that significant work still needs to be undertaken to make the portals examples of 'best practice' e-government services.

Fault Identification Through the Combination of Symbolic Conflict Recognition and Markov Chain-Aided Belief Revision

Fault identification is a search for possible behaviors that would explain the observed behavior of a physical system. During this search, different possible models are considered and information about the interaction between possible behaviors is derived. Much of this potentially useful information is generally ignored in conventional pure symbolic approaches to fault diagnosis, however. A novel approach is presented in this paper that exploits uncertain information on the behavioral description of system components to identify possible fault behaviors in physical systems. The work utilizes the standard conflict recognition technique developed in the framework of the general diagnostic engine (GDE) to support diagnostic inference through the production of both rewarding and penalizing evidence. In particular, Markov matrices are derived from the given evidence, thereby enabling the use of Markov chains to implement the diagnostic process. This work has resulted in a technique, which maximizes the use of derived information, for identifying candidates for multiple faults that is demonstrated to be very effective.

A New KE-Free Online ICALL System Featuring Error Contingent Feedback

As a first step towards implementing a human language teacher, we have developed a new template-based on-line ICALL (intelligent computer assisted language learning) system capable of automatically diagnosing learners' free-format translated inputs and returning error contingent feedback. The system architecture we have adopted allows language teachers to build their own expertise into the system without the help of KEs (knowledge engineers), thus alleviating the long-standing KE bottlenecks associated with conventional expert-systems-based ITS (intelligent tutoring system) or ICALL (Murray, 1999). The core of the system comprises a unique FSA (finite state automaton)-based template knowledge base system, a robust and global HCS (heaviest common sequence)-based diagnostic engine, a POST(part-of speech-tagged) parser and related learners' model as well as an easy-to-use VTAT (visual template authoring tool). To simplify the task of authoring often quite complex template patterns, we have developed two sets of simpler rules; the first group of rules allows language teachers to manipulate complex sentence patterns with ease by constructing a template-template representation from which numerous separate templates can be extracted. The second group of buggy rules can be used to automatically generate syntactic bugs for learners by replacing part of the correct syntactic rules with plausible buggy rules. Using participants' responses extracted into the system templates, we present some convincing experimental verifications that the diagnostic engine is capable of providing error-contingent feedback and diagnosis applicable to a wide range of learners with differing educational backgrounds.