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.
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.
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.
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.
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.
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.
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.
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.
Analog circuits need more effective fault diagnosis methods. In this study, the fault diagnosis method of analog circuits was studied. The fault feature vectors were extracted by a wavelet transform and then classified by a generalized regression neural network (GRNN). In order to improve the classification performance, a wolf pack algorithm (WPA) was used to optimize the GRNN, and a WPA-GRNN diagnosis algorithm was obtained. Then a simulation experiment was carried out taking a Sallen–Key bandpass filter as an example. It was found from the experimental results that the WPA could achieve the preset accuracy in the eighth iteration and had a good optimization effect. In the comparison between the GRNN, genetic algorithm (GA)-GRNN and WPA-GRNN, the WPA-GRNN had the highest diagnostic accuracy, and moreover it had high accuracy in diagnosing a single fault than multiple faults, short training time, smaller error, and an average accuracy rate of 91%. The experimental results prove the effectiveness of the WPA-GRNN in fault diagnosis of analog circuits, which can make some contributions to the further development of the fault diagnosis of analog circuits.
Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.
The presence of an open-circuit fault subjects a three-phase induction motor to severely unbalanced voltages that may damage the stator windings consecutively causing total shutdown of systems. Unplanned downtime is very costly. Therefore, fault diagnosis is essential for making a predictive plan for maintenance and saving the required time and cost. This paper presents a model-based diagnosis technique for diagnosing an open-circuit fault in any phase of a three-phase induction motor. The proposed strategy requires only current signals from the faulty machine to compare them with the healthy currents from an induction motor model. Then the errors of comparison are used as an objective function for a genetic algorithm that estimates the parameters of a healthy model, which they employed to identify and localize the fault. The simulation results illustrate the behaviours of basic parameters (stator and rotor resistances, self-inductances, and mutual inductance) and the number of stator winding turn parameters with respect to the location of an open-circuit fault. The results confirm that the number of stator winding turns are the useful parameters and can be utilized as an identifier for an open-circuit fault. The originality of this work is in extracting fault diagnosis features from the variations of the number of stator winding turns.
Incidence of colonic atresia in living infants ranges from 1:5,000 to 1:60,000 (average 1:20,000). It constitutes 1.8 to 15% of all cases of atresia of the gastrointestinal tract. In 58.56–75% of all cases is right-sided. We aim, through the presentation of two cases of colonic atresia which we encountered and after systematic research of the current literature, at addressing three major issues: diagnostic approach, operative strategy and management of the prognostic parameters of the colonic atresia. The common parameter in these two cases was the early diagnosis, which played a significant role in the uncomplicated postoperative course. The first case was a type I sigmoid atresia. Contrast’s escape during contrast enema examination due to accidental rupture of the distal part of the colon led to diagnosis. Side-to-side anastomosis, restoration of the rupture and a central loop sigmoidostomy were urgently performed. The second case was a type III atresia at the level of the ascending colon, which was early diagnosed via pregenital ultrasonography, in which colonic dilation was depicted. Restoration of the intestinal continuity early after birth was performed at a time. In conclusion, we believe that early diagnosis, selection of the appropriate operative strategy and prompt recognition of potential post-operative complications, especially rupture of the anastomosis, contribute to the optimization of the prognosis in patients with colonic atresia.
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.
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.
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.
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.