The paper presents a heuristic approach to the problem of analog circuit diagnosis. Different optimization techniques in the field of test point selection are discussed. Two new algorithms: SALTO and COSMO have been introduced. Both searching procedures have been implemented in a form of the expert system in PROLOG language. The proposed methodologies have been exemplified on benchmark circuits. The obtained results have been compared to the others achieved by different approaches in the field and the benefits of the proposed methodology have been emphasized. The inference engine of the heuristic algorithms has been presented and the expert system knowledge-base construction discussed.
An elaborate study executed in the direction of exploring energy saving potential shows that more than 20% of electrical energy used in industry is used for pump systems. Experts calculate that more than 30% of this energy can be saved by improving control and diagnosis for pump systems. Unfortunately, the application ratio of such system is small and consequently a large demand for such technological advanced systems can still be observed in the pump industry. Because of this reason and still growing demand of saving energy in industry, two Universities in Germany and Switzerland together with leading German pump manufacturer decided to join their knowledge and skill to work on the project called "Smart Pump". This paper presents one of the first results of this project, which goal is the development of future control methods and diagnosis systems for intelligent pumps.
Hypertension constitutes one of the most common diseases leading patients to the Outpatient Departments. Idiopathic hypertension is the prevailing type, but on the other hand, the possible presence of clinical entities responsible for the development of secondary hypertension should never be underestimated. We retrospectively studied 447 subjects aged between 20 and 84 years old and diagnosed with hypertension, who were thoroughly evaluated for secondary hypertension. Our analysis demonstrated that 35 out of the 447 subjects were fi nally diagnosed with secondary hypertension, representing a relative frequency of 7.8%. Most common causes of secondary hypertension identifi ed in our study group were: glucocorticoid intake (n = 14), obesity hypoventilation syndrome (n = 6), obstructive sleep apnea (n = 2) and preeclamspia (n = 2). Several other causes are also reported. Our study, conducted in a single center in Northern Greece, confi rms previous reports concerning the prevalence of secondary hypertension among Greek patients, shedding light on potential pathophysiologic mechanisms. In conclusion, a high proportion of hypertensive individuals still feature have an underlying cause, thus, diagnostic work-up should be thorough and exhaustive, in order the correct diagnosis to be made and the targeted treatment to be initiated.
This paper is devoted to measuring the continuous diagnosis capability of a system. A key metric and its calculation models are proposed enabling us to measure the continuous diagnosis capability of a system directly without establishing and searching the sequential fault tree (SFT) of the system. At first a description of a D matrix is given and its metric is defined to determine the weakness of a continuous diagnosis. Then based on the definition of a sequential fault combination, a sequential fault tree (SFT) is defined with its establishment process summarized. A key SFT metric is established to measure the continuous diagnosis capability of a system. Two basic types of dependency graphical models (DGMs) and one combination type of DGM are selected for characteristics analysis and establishment of metric calculation models. Finally, both the SFT searching method and direct calculation method are applied to two designs of one type of an auxiliary navigation equipment, which shows the high efficiency of the direct calculation method.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
The attenuating properties of biological tissue are of great importance in ultrasonic medical imaging. Investigations performed in vitro and in vivo showed the correlation between pathological changes in the tissue and variation of the attenuation coefficient. In order to estimate the attenuation we have used the downshift of mean frequency (fm) of the interrogating ultrasonic pulse propagating in the medium. To determine the fm along the propagation path we have applied the fm estimator (I/Q algorithm adopted from the Doppler mean frequency estimation technique). The mean-frequency shift trend was calculated using Single Spectrum Analysis. Next, the trends were converted into attenuation coefficient distributions and finally the parametric images were computed. The RF data were collected in simulations and experiments applying the synthetic aperture (SA) transmit-receiving scheme. In measurements the ultrasonic scanner enabling a full control of the transmission and reception was used. The resolution and accuracy of the method was verified using tissue mimicking phantom with uniform echogenicity but varying attenuation coefficient.
Anaphylaxis is an increasing problem in public health. Th e food allergens (mainly milk, eggs, and peanuts) are the most frequent cause of anaphylaxis in children and youth. In order to defi ne the cause of anaphylaxis, skin tests, the determination of the concentration of specifi c IgE in the blood and basophil activation test are conducted. In vitro tests are preferred due to the risk of allergic response during in vivo tests. Component-resolved diagnosis (CRD) is an additional tool in allergology, recommended in the third level of diagnostics when there are diagnostic doubts aft er the above mentioned tests have been carried out. The paper presents 3 cases of patients with anaphylactic response, and the application of CRD in these patients helped in planning the treatment. Patient 1 is a 4-year-old boy with diagnosed atopic dermatitis and bronchial asthma reported an anaphylactic shock at the age of seven months caused by cow’s milk and the exacerbation of bronchial asthma aft er eating some fruit. Patient 2 is a 35-year-old woman who has had anaphylactic shock three times: in June 2015, 2016, and 2017 and associates these episodes with the consumption of dumplings with a caramel, bun, and the last episode took place during physical exertion few hours aft er eating waffl e. Patient 3 is a 26-year-old man with one-time loss of consciousness after eating mixed nuts and drinking beer. CRD off ers the possibility to conduct a detailed diagnostic evaluation of patients with a history of anaphylactic reaction.
In this study, a preliminary evaluation was made of the applicability ofthe signalsof the cutting forces, vibration and acoustic emission in diagnosis of the hardness and microstructure of ausferritic ductile iron and tool edge wear rate during its machining. Tests were performed on pearlitic-ferritic ductile iron and on three types of ausferritic ductile iron obtained by austempering at 400, 370 and 320⁰C for 180 minutes. Signals of the cutting forces (F), vibration (V) and acoustic emission (AE) were registered while milling each type of the cast iron with a milling cutter at different degrees of wear. Based on individual signals from all the sensors, numerous measures were determined such as e.g. the average or maximum signal value. It was found that different measures from all the sensors tested depended on the microstructure and hardness of the examined material, and on the tool condition. Knowing hardness of the material and the cutting tool edge condition, it is possible to determine the structure of the material .Simultaneous diagnosis of microstructure, hardness, and the tool condition is probably feasible, but it would require the application of a diagnostic strategy based on the integration of numerous measures, e.g. using neural networks.
In the paper modeling of main inductances for mathematical models of induction motors is applied to study the effects caused by a rotor eccentricity and saturation effects. All three possible types of eccentricity: static, dynamic and mixed are modeled. The most important parameters describing rotor eccentricity include self and mutual inductances of the windings. The structural changes of the permeance function as a result of eccentricity appearance and the Fourier spectra of inductances in occurrence of saturation for each case are determined in the paper. The presented algorithm can be used for the diagnostically specialized models of induction motors.
The paper deals with multiple soft fault diagnosis of analogue circuits. A method for diagnosis of linear circuits is developed, belonging to the class of the fault verification techniques. The method employs a measurement test performed in the frequency domain, leading to the nonlinear least squares problem. To solve this problem the Powell minimization method is applied. The diagnostic method is adapted to real circumstances, taking into account deviations of fault-free parameters and measurement uncertainty. Two examples of electronic circuits encountered in practice demonstrate that the method is efficient for diagnosis of middle-sized circuits. Although the method is dedicated to linear circuits it can be adapted to multiple soft fault diagnosis of nonlinear ones. It is illustrated by an example of a CMOS circuit designed in a sub-micrometre technology.
The paper is aimed at presenting a study of the main limitations and problems influencing the robustness of diagnostic algorithms used in diagnostics of complex chemical processes and to present the selected exemplary solutions of how to increase it. The five major problems were identified in the study. They are associated with: uncertainties of fault detection and reasoning, changes of the diagnosed process structure, delays of fault symptoms formation and multiple faults. A brief description and exemplary solutions allowing increase of the robustness of diagnostic algorithms were given. Proposed methods were selected keeping in mind applicability for the on-line monitoring and diagnostics of complex chemical processes.
This paper deals with multiple soft fault diagnosis of nonlinear analog circuits comprising bipolar transistors characterized by the Ebers-Moll model. Resistances of the circuit and beta forward factor of a transistor are considered as potentially faulty parameters. The proposed diagnostic method exploits a strongly nonlinear set of algebraic type equations, which may possess multiple solutions, and is capable of finding different sets of the parameters values which meet the diagnostic test. The equations are written on the basis of node analysis and include DC voltages measured at accessible nodes, as well as some measured currents. The unknown variables are node voltages and the parameters which are considered as potentially faulty. The number of these parameters is larger than the number of the accessible nodes. To solve the set of equations the block relaxation method is used with different assignments of the variables to the blocks. Next, the solutions are corrected using the Newton-Raphson algorithm. As a result, one or more sets of the parameters values which satisfy the diagnostic test are obtained. The proposed approach is illustrated with a numerical example.
The paper deals with fault diagnosis of nonlinear analogue integrated circuits. Soft spot short defects are analysed taking into account variations of the circuit parameters due to physical imperfections as well as self-heating of the chip. A method enabling to detect, locate and estimate the value of a spot defect has been developed. For this purpose an appropriate objective function was minimized using an optimization procedure based on the Fibonacci method. The proposed approach exploits DC measurements in the test phase, performed at a limited number of accessible points. For illustration three numerical examples are given.
The paper deals with a multiple fault diagnosis of DC transistor circuits with limited accessible terminals for measurements. An algorithm for identifying faulty elements and evaluating their parameters is proposed. The method belongs to the category of simulation before test methods. The dictionary is generated on the basis of the families of characteristics expressing voltages at test nodes in terms of circuit parameters. To build the fault dictionary the n-dimensional surfaces are approximated by means of section-wise piecewise-linear functions (SPLF). The faulty parameters are identified using the patterns stored in the fault dictionary, the measured voltages at the test nodes and simple computations. The approach is described in detail for a double and triple fault diagnosis. Two numerical examples illustrate the proposed method.
This paper is devoted to multiple soft fault diagnosis of analog nonlinear circuits. A two-stage algorithm is offered enabling us to locate the faulty circuit components and evaluate their values, considering the component tolerances. At first a preliminary diagnostic procedure is performed, under the assumption that the non-faulty components have nominal values, leading to approximate and tentative results. Then, they are corrected, taking into account the fact that the non-faulty components can assume arbitrary values within their tolerance ranges. This stage of the algorithm is carried out using the linear programming method. As a result some ranges are obtained including possible values of the faulty components. The proposed approach is illustrated with two numerical examples.
Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By identifying combinations of faults in a logical framework it’s possible to define the structure of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic tools is in their ability to reasoning under uncertainty. Some typical constraints to the fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper shows that information processing has become simple and easy through the use of Bayesian networks. The study presented showed that updating knowledge and exploiting new knowledge does not complicate calculations. The contribution is the structural approach of faults diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are defined in descending order. The approach presented in this paper has been successfully applied to turbo compressor, which represent vital equipment in petrochemical plant.
A new soft-fault diagnosis approach for analog circuits with parameter tolerance is proposed in this paper. The approach uses the fuzzy nonlinear programming (FNLP) concept to diagnose an analog circuit under test quantitatively. Node-voltage incremental equations, as constraints of FNLP equation, are built based on the sensitivity analysis. Through evaluating the parameters deviations from the solution of the FNLP equation, it enables us to state whether the actual parameters are within tolerance ranges or some components are faulty. Examples illustrate the proposed approach and show its effectiveness.
The paper deals with the problems of designing observers and unknown input observers for discrete-time Lipschitz non-linear systems. In particular, with the use of the Lyapunov method, three different convergence criteria of the observer are developed. Based on the achieved results, three different design procedures are proposed. Then, it is shown how to extend the proposed approach to the systems with unknown inputs. The final part of the paper presents illustrative examples that confirm the effectiveness of the proposed techniques. The paper also presents a MATLAB® function that implements one of the design procedures.
Pigmented villonodular synovitis (PVNS) is a benign disease that rarely undergoes malignant transformation. Th ere are two types of disease: localized (nodular tenosynovitis) and diff used (pigmented villonodular synovitis/tenosynovitis) with intra- or extra-articular locations. Th e second one is limited to synovium of the burse (PVNB) or tendon sheath (PVNTS). Th e intraarticular lesions are usually located in the knee, hip, ankle and elbow joints. Histologically, PVNS is a tenosynovial giant cell tumor, characterized by proliferation of two types of mononuclear cells — predominantly small, histiocyte-like cells and larger cells with dense cytoplasm, reniform or lobulated nucleus, with accompanying multinucleated giant cells and macrophages overloaded with hemosiderin that give typical image on MRI — currently selected as a gold standard for its diagnosis. Th e classic X-ray and CT are non-specifi c but similar to ultrasound should be used to evaluate disease progression and treatment response if radiotherapeutic and pharmacological methods were selected for treatment. An open arthroscopic surgery could also be applied in selected cases.
I n t r o d u c t i o n: Hypoplastic left heart syndrome (HLHS) is a congenital heart anomaly that is diagnosed prenatally or postnatally. The prenatal diagnosis leads to limiting the rate of systemic complications in the preoperative period due to optimization of the early therapeutic management. O b j e c t i v e: The objective of the study is to determine the effect of prenatal diagnostic management of HLHS on the condition of newborns and the frequency of antibiotherapy employment prior to the first stage of surgical treatment. Me t h o d o l o g y: The study included 95 children with HLHS operated on in the years 2014–2016. The cohort was divided into two groups: newborns with a prenatally diagnosed heart defect (50 children — 52.6%) and neonates with the defect diagnosed after birth (45 children — 47.4%). The data of the patients were analyzed based on their medical records. R e s u l t s: The mean age of the children upon admission was 3.86 days in the group of patients with the prenatally diagnosed heart defect (PreHLHS) and 7.41 days in the group of newborns without the prenatal diagnosis (PostHLHS) (p = 0.001). In 60% of the PreHLHS group patients (30/50), at least one antibiotic was administered, while in the PostHLHS group, antibiotherapy was employed in 93.3% (42/45) cases (p = 0.001). Bacteriological tests demonstrated pathogen growth in 33 children (36% and 33.3%, respectively), what accounted for 34.7% of the entire cohort. On the average, the first antibiotic was introduced on the 6.55th day of life in the PreHLHS group and on the 2.73th day in the PostHLHS group (p = 0.005). Th e most profound differences in antibiotic employment involved aminoglycosides. The aforementioned type of antibiotic medications was administered to 6% of the children with the prenatal diagnosis and to 17.8% of the children diagnosed postnatally (p = 0.042). C o n c l u s i o n s: Preoperative antibiotherapy in children with HLHS was employed more frequently than it would be indicated by microbiology tests results. Antibiotics were observed to be introduced more commonly and earlier in the newborns with the postnatally diagnosed congenital heart defect.
Current methods of fault diagnosis for the grounding grid using DC or AC are limited in accuracy and cannot be used to identify the locations of the faults. In this study, a new method of fault diagnosis for substation grounding grids is proposed using a square-wave. A frequency model of the grounding system is constructed by analyzing the frequency characteristics of the soil and the grounding conductors into which two different frequency square-wave sources are injected. By analyzing and comparing the corresponding information of the surface potentials of the output signals, the faults of the grounding grid can be diagnosed and located. Our method is verified by software simulation, scale model experiments and field experiments.
This paper presents a Kalman filter based method for diagnosing both parametric and catastrophic faults in analog circuits. Two major innovations are presented, i.e., the Kalman filter based technique, which can significantly improve the efficiency of diagnosing a fault through an iterative structure, and the Shannon entropy to mitigate the influence of component tolerance. Both these concepts help to achieve higher performance and lower testing cost while maintaining the circuit.s functionality. Our simulations demonstrate that using the Kalman filter based technique leads to good results of fault detection and fault location of analog circuits. Meanwhile, the parasitics, as a result of enhancing accessibility by adding test points, are reduced to minimum, that is, the data used for diagnosis is directly obtained from the system primary output pins in our method. The simulations also show that decision boundaries among faulty circuits have small variations over a wide range of noise-immunity requirements. In addition, experimental results show that the proposed method is superior to the test method based on the subband decomposition combined with coherence function, arisen recently.