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.
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.
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.
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.
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.
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.
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.
B a c k g r o u n d: Assessment of the neurocontrol of the external anal sphincter has long been restricted to investigating patients by invasive tools. Less invasive techniques have been regarded less uitable for diagnosis. O b j e c t iv e: The aim was to develop a surface electromyography-based algorithm to facilitate fecal incontinence diagnosis, and to assess its sensitivity and specificity. D e s i g n: Data analysis from a single center prospective study. P a t i e n t s: All patients from colorectal surgery office were considered. They underwent a structured interview, a general physical and proctologic examination. Patients with diagnosed fecal incontinence (Fecal Incontinence Severity Index >10) were included into the study group. The control group consisted of healthy volunteers that scored 5 or less and had negative history and physical exam. Both groups underwent the same tests (rectoscopy, anorectal manometry, transanal ultrasonography, multichannel surface electromyography and assessment of anal reflexes). M e t h o d s: EMG results were analyzed to find parameters that would facilitate fecal incontinence diagnosis. O u t c o m e m e a s u r e s: Sensitivity and specificity of surface electromyography, to diagnose fecal incontinence, were assessed. R e s u l t s: A total of 49 patients were included in the study group (mean age ± SD 58.9 ± 13.8). The control group (n = 49) gender matched the study group (mean age ± SD 45.4 ± 15.1). The constructed classification tree, based on surface electromyography results, correctly classified 97% of cases. The sensitivity and specificity of this classification tree, to diagnose FI, was 96% and 98% respectively. L i m i t a t i o n s: The age of women in the control group differs significantly from mean age of other groups. C o n c l u s i o n s: Surface electromyography is an good tool to facilitate diagnosing of fecal incontinence.
This article presents a computer system for the identification of casting defects using the methodology of Case-Based Reasoning. The system is a decision support tool in the diagnosis of defects in castings and is designed for small and medium-sized plants, where it is not possible to take advantage of multi-criteria data. Without access to complete process data, the diagnosis of casting defects requires the use of methods which process the information based on the experience and observations of a technologist responsible for the inspection of ready castings. The problem, known and studied for a long time, was decided to be solved with a computer system using a CBR (CaseBased Reasoning) methodology. The CBR methodology not only allows using expert knowledge accumulated in the implementation phase, but also provides the system with an opportunity to "learn" by collecting new cases solved earlier by this system. The authors present a solution to the system of inference based on the accumulated cases, in which the main principle of operation is searching for similarities between the cases observed and cases stored in the knowledge base.
This article presents combined approach to analog electronic circuits testing by means of evolutionary methods (genetic algorithms) and using some aspects of information theory utilisation and wavelet transformation. Purpose is to find optimal excitation signal, which maximises probability of fault detection and location. This paper focuses on most difficult case where very few (usually only input and output) nodes of integrated circuit under test are available.
This paper presents methods for optimal test frequencies search with the use of heuristic approaches. It includes a short summary of the analogue circuits fault diagnosis and brief introductions to the soft computing techniques like evolutionary computation and the fuzzy set theory. The reduction of both, test time and signal complexity are the main goals of developed methods. At the before test stage, a heuristic engine is applied for the principal frequency search. The methods produce a frequency set which can be used in the SBT diagnosis procedure. At the after test stage, only a few frequencies can be assembled instead of full amplitude response characteristic. There are ambiguity sets provided to avoid a fault tolerance masking effect.
Statistical Process Control (SPC) based on the Shewhart’s type control charts, is widely used in contemporary manufacturing industry, including many foundries. The main steps include process monitoring, detection the out-of-control signals, identification and removal of their causes. Finding the root causes of the process faults is often a difficult task and can be supported by various tools, including datadriven mathematical models. In the present paper a novel approach to statistical control of ductile iron melting process is proposed. It is aimed at development of methodologies suitable for effective finding the causes of the out-of-control signals in the process outputs, defined as ultimate tensile strength (Rm) and elongation (A5), based mainly on chemical composition of the alloy. The methodologies are tested and presented using several real foundry data sets. First, correlations between standard abnormal output patterns (i.e. out-of-control signals) and corresponding inputs patterns are found, basing on the detection of similar patterns and similar shapes of the run charts of the chemical elements contents. It was found that in a significant number of cases there was no clear indication of the correlation, which can be attributed either to the complex, simultaneous action of several chemical elements or to the causes related to other process variables, including melting, inoculation, spheroidization and pouring parameters as well as the human errors. A conception of the methodology based on simulation of the process using advanced input - output regression modelling is presented. The preliminary tests have showed that it can be a useful tool in the process control and is worth further development. The results obtained in the present study may not only be applied to the ductile iron process but they can be also utilized in statistical quality control of a wide range of different discrete processes.
The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes.
Wind energy has achieved prominence in renewable energy production. There fore, it is necessary to develop a diagnosis system and fault-tolerant control to protect the system and to prevent unscheduled shutdowns. The presented study aims to provide an experimental analysis of a speed sensor fault by hybrid active fault-tolerant control (AFTC) for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). The hybrid AFTC switches between a traditional controller based on proportional integral (PI) controllers under normal conditions and a robust backstepping controller system without a speed sensor to avoid any deterioration caused by the sensor fault. A sliding mode observer is used to estimate the PMSG rotor position. The proposed controller architecture can be designed for performance and robustness separately. Finally, the proposed methodwas successfully tested in an experimental set up using a dSPACE 1104 platform. In this experimental system, the wind turbine with a generator connection via a mechanical gear is emulated by a PMSM engine with controled speed through a voltage inverter. The obtained experimental results show clearly that the proposed method is able to guarantee service production continuity for the WECS in adequate transition.