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EN
Increased performance of hydraulic drive components, as well as easier maintenance and diagnostics, can be achieved through the use of intelligent devices. Introducing sensors, electronic blocks and control algorithms into the equipment will enable easier repairs in the case of failure, or can increase the efficiency of the installation by providing selected operating parameters to the machine controller. In the case of a malfunction, the smart device can provide error codes. Smart devices can receive and send via various communication protocols (RS232, CAN, Fieldbus, Modbus) commands and feedback signals of monitored parameters. This paper presents the construction of such a monitoring and diagnostics module, the test application and the obtained charts.
PL
Zwiększenie wydajności podzespołów hydraulicznych instalacji napędowych, jak również łatwiejsza konserwacja i diagnostyka mogą zostać osiągnięte poprzez zastosowanie inteligentnych urządzeń. Wprowadzenie czujników, bloków elektronicznych i algorytmów sterowania do urządzeń, umożliwi łatwiejsze naprawy w przypadku awarii lub może przyczynić się do zwiększenia wydajności instalacji dzięki dostarczeniu do sterownika maszyny wybranych parametrów roboczych. W przypadku nieprawidłowego działania, urządzenie inteligentne może dostarczyć kody błędów. Inteligentne urządzenia mogą odbierać i wysyłać przez różne protokoły komunikacyjne (RS232, CAN, Fieldbus, Modbus) polecenia i sygnały zwrotne monitorowanych parametrów. W artykule przedstawiono budowę takiego modułu monitoringu i diagnostyki, aplikację testową oraz uzyskane wykresy.
EN
In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multi-source information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequency-domain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multi-feature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods.
EN
Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.
EN
Multilevel inverters have been widely used in various occasions due to their advantages such as low harmonic content of the output waveform. However, because multilevel inverters use a large number of devices, the possibility of circuit failure is also higher than that of traditional inverters. A T-type three-level inverter is taken as the research object, anda diagnostic study is performed on the open-circuit fault of insulated gate bipolar transistor (IGBT) devices in the inverter. Firstly, the change of the current path in the inverter when anopen-circuit fault of the device occurred, and the effect on the circuit switching states andthe bridge voltages were analyzed. Then comprehensively considered the bridge voltages,and proposed a fault diagnosis method for a T-type three-level inverter based on specificfault diagnosis signals. Finally, the simulation verification was performed. The simulation results prove that the proposed method can accurately locate the open-circuit fault of theinverter device, and has the advantage of being easy to implement.
EN
The paper focuses on active fault diagnosis (AFD) of large scale systems. The multiple model framework is considered and two architectures are treated: the decentralized and the distributed one. An essential part of the AFD algorithm is state estimation, which must be supplemented with a mechanism to achieve feasible implementation in the multiple model framework. In the paper, the generalized pseudo Bayes and interacting multiple model estimation algorithms are considered. They are reformulated for a given model of a large scale system. Performance of both AFD architectures is analyzed for different combinations of multiple model estimation algorithms using a numerical example.
EN
Worm gearboxes (WG) are often preferred, because of their high torque, quickly reducing speed capacity and good meshing effectiveness, in many industrial applications. However, WGs may face with some serious problems like high temperature at the speed reducer, gear wearing, pitting, scoring, fractures and damages. In order to prevent any damage, loss of time and money, it is an important issue to detect and classify the faults of WGs and develop the maintenance plans accordingly. The present study addresses the application of the deep learning method, convolutional neural network (CNN), in the field of thermal imaging that were gathered from a test rig operating on different loads and speeds. Deep learning approaches, have proven their powerful capability to exploit faulty information from big data and make intelligently diagnostic decisions. Studies concerning the condition monitoring of WGs in the literature are limited. This is the first study on WGs with infrared thermography rather than vibration and sound measurements which have some deficiencies about hardware requirements, restricted measurement abilities and noisy signals. For comparison, CNN was also trained, with vibration and sound data which were collected from the healthy and faulty WGs. The results of fault diagnosis show that thermal image based CNN model on WG has achieved 100% success rate whereas the vibration performance was 83.3 % and sound performance was 81.7%. As a result, thermal image based CNN model showed a better diagnosing performance than the others for WGs. Moreover, condition monitoring of WGs, can be performed correctly with less measurement costs via thermal imaging methods.
PL
W wielu zastosowaniach przemysłowych preferuje się przekładnie ślimakowe, ze względu na ich wysoki moment obrotowy, możliwość szybkiej redukcji prędkości i dobrą sprawność zazębienia. Jednakże przekładnie tego typu narażone są często na poważne problemy, takie jak wysoka temperatura przy reduktorze prędkości czy też zużycie, pitting (wżery), zatarcie, pęknięcie lub uszkodzenie kół zębatych. Zapobiec takim uszkodzeniom, i związanym z nimi stratom finansowym i czasowym, można poprzez wykrywanie i klasyfikowanie błędów przekładni i odpowiednie opracowanie planów konserwacji. Niniejsze badanie dotyczy zastosowania metody głębokiego uczenia oraz splotowych sieci neuronowych (SSN) do monitoringu stanu przekładni na podstawie termogramów zarejestrowanych na stanowisku testowym pracującym przy różnych obciążeniach i prędkościach. Podejścia oparte na uczeniu głębokim umożliwiają efektywne wykorzystanie informacji o błędach pochodzących z dużych zbiorów danych i podejmowanie trafnych decyzji diagnostycznych. Niewiele z dostępnych publikacji poświęconych jest monitorowaniu stanu przekładni ślimakowych. Niniejsza praca jako pierwsza przedstawia badania przekładni ślimakowej z zastosowaniem termografii zamiast zwyczajowo prowadzonych pomiarów drgań i dźwięku, które mają pewne wady dotyczące wymagań sprzętowych, ograniczonych możliwości pomiarowych i głośności sygnałów. SNN opartą na danych termicznych porównano z siecią, którą uczono na zbiorach danych wibracyjnych i akustycznych pochodzących z prawidłowo działających i uszkodzonych przekładni ślimakowych. Wyniki diagnostyki uszkodzeń pokazują, że model SSN przekładni ślimakowej oparty na obrazie termicznym osiągnął stuprocentową (100%) skuteczność, podczas gdy skuteczność modeli opartych na danych wibracyjnych i akustycznych wyniosła, odpowiednio, 83,3% i 81,7%. Tym samym, model SNN oparty na obrazie termicznym pozwalał na trafniejsze diagnozowanie przekładni ślimakowej niż pozostałe modele. Ponadto zastosowanie metod opartych na termografii pozwala na poprawne monitorowanie stanu przy niższych kosztach pomiaru.
EN
The imperative of quality and productivity has increased the complexity of technological processes, posing the problem of reliability. Today, fault diagnosis remains a very important task because of its essential role in improving reliability, but also in minimizing the harmful consequences that can be catastrophic for the safety of equipment and people. Indeed, an effective diagnosis not only improves reliability, but also reduces maintenance costs. Systems in which dynamic behaviour evolves as a function of the interaction between continuous dynamics and discrete dynamics, present in the system, are called hybrid systems. The goal is to develop monitoring and diagnostic procedures to the highest level of control to ensure safety, reliability and availability objectives. This article presents an approach to the diagnosis of hybrid systems using hybrid automata and neural-fuzzy system. The use of the neural-fuzzy system allows modeling the continuous behaviour of the system. On the other hand, the hybrid automata gives a perfect estimate of the discrete events and make it possible to execute a fault detection algorithm mainly consists of classifying the appeared defects. On the implementation plan, the results were applied in a water desalination plant.
EN
Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in IM applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.
EN
Despite technological advances and progress in industrial systems, the fault diagnosis of a system remains a very important task. In fact an effective diagnosis contributes not only to improved reliability but also to a decrease in maintenance costs. This paper presents an approach to a diagnosis of hybrid systems thanks to the use of Bond Graphs, Observer and Timed Automata. Dynamic models (in normal and failing mode) are generated by an observer based methods as well as through state equations generated by the Bond Graphs model. The procedure of fault localization through a method based on the observer does not allow locating faults with the same signature of failure. Thus the diagnosis technique for the localization of these defects will be based on the time analysis using Timed Automata. The proposed approach is then validated by simulation tests in a two tanks hydraulic system.
EN
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.
EN
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.
EN
Introduced through policy instruments, as well as due to increase awareness of and demand for energy, alternative, renewable energy sources are becoming increasingly popular and necessary. The growing market and standards are forcing producers of renewable energy sources to constantly improve the quality of their products. Biomass trigenerators are one way of obtaining such energy, both in the form of electricity, heat and cold. These are elements generating steam by burning various solid, liquid or gaseous fuels of organic origin. Rotating machines in the form of turbines or steam engines are used to generate electricity. Unfortunately, they are particularly exposed to steam impacts associated with discontinuous work. This article presents the monitoring and prevention system for such impacts. It is based on the analysis of the frequency spectrum of vibrations of such generators and can be used to implement a trigenerator control system that will reduce the influence of such impacts. With proposed diagnostic system, the efficiency and life span of a Renewable Energy Source can increase significantly.
13
EN
The article presents an overview of methods for formulating diagnostic equations in linear analog circuits. In the case of dynamic circuits, the creation of a diagnostic equation based on the description of systems in the time domain and frequency domain is discussed. A unified and systematized description of different classes of linear circuits leads to the general test equation and allows for the use of the general procedure for solving it. The proposed methodology allows locating and identifying single and multiple parametric faults. For illustraton of the method the diagnosis of a linear electronic circuit is discussed.
PL
W artykule przedstawiono przegląd metod formułowania równań diagnostycznych w liniowych układach analogowych. W przypadku obwodów dynamicznych omówiono tworzenie równania diagnostycznego na podstawie opisu układów w dziedzinie czasu i częstotliwości. Ujednolicony i usystematyzowany opis różnych klas obwodów liniowych prowadzi do ogólnego równania diagnostycznego oraz pozwala na zastosowanie ogólnej procedury jego rozwiązywania. Zaproponowana metodologia umożliwia lokalizację i identyfikację pojedynczych oraz wielokrotnych uszkodzeń parametrycznych. Działanie metody zilustrowano na przykładowym układzie elektronicznym.
EN
To improve the reliability of motor system, this paper investigates the sensor fault diagnosis methods for T-type inverter-fed dual three-phase permanent magnet synchronous motor (PMSM) drives. Generally, a T-type three-level inverter-fed dual three-phase motor drive utilizes four phase-current sensors, two direct current (DC)-link voltage sensors and one speed sensor. A series of diagnostic methods have been comprehensively proposed for the three types of sensor faults. Both the sudden error change and gradual error change of sensor faults are considered. Firstly, the diagnosis of speed sensor fault was achieved by monitoring the error between the rotating speed of stator flux and the value from speed sensor. Secondly, the large high-frequency voltage ripple of voltage difference between the estimated voltage and the reference voltage was used to identify the voltage sensor faults, and the faulty voltage sensor was determined according to the deviation of voltage difference. Thirdly, the abnormal current amplitude on harmonic subspace was adopted to identify the current sensor faults, and the faulty current sensor was located by distinguishing the current trajectory on harmonic subspace. The experiments have been taken on a laboratory prototype to verify the effectiveness of the proposed fault diagnosis schemes.
EN
It is difficult to diagnose a three-phase matrix converter using a mathematical model, because a matrix converter consists of nine switches with various nonlinear factors. Since a neural network does not require any mathematical system model, it is a suitable technique for fault classification in matrix converters. In this manuscript, a fault diagnostic system for three-phase to three-phase matrix converters using a neural network is proposed to detect a fault and identify its location. The proposed diagnostic system can detect faults using just one phase current waveform which is very efficient in terms of cost of sensors and system complexity. This method was evaluated using simulation and experimental data sets in two scenarios. The results confirm that in different normal and abnormal situations the system achieves performance levels in excess of 98%.
EN
One of the most important subsystems of the vehicles and machines operating currently in industry and transportation are the rotating subsystems. During the operation, due to the forcing factors influence, the technical state of them is changing and the failure can occur. Fault diagnosis is maintenance task considered as an essential in such subsystems, since possibility of an early detection and diagnosis of the faulty condition can save both time and money. To do this the analysis of the subsystems vibrations is performed. The identified technical state should be considered in a context of the ability and different inability states. Therefore, the first step of the diagnostic procedure is the ability and different inability states identification. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for both expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This article presents authors initial research in deep learning-based data-driven fault diagnosis of rotating subsystems. The proposed technique input raw three-axis accelerometer signal as high-definition image into deep learning layers, which automatically extract signal features, enabling high classification accuracy.
EN
The use of condition monitoring techniques in wind energy has been recently growing and the average unavailability time of an operating wind turbine in an industrial wind farm is estimated to be less than the 3%. The most powerful approach for gearbox condition monitoring is vibration analysis, but it should be noticed as well that the collected data are complex to analyse and interpret and that the measurement equipment is costly. For these reasons, several wind turbine subcomponents are monitored through temperature sensors. It is therefore valuable developing analysis techniques for this kind of data, with the aim of detecting incoming faults as early as possible. On these grounds, the present work is devoted to a test case study of wind turbine generator slip ring damage detection. A principal component regression is adopted, targeting the temperature collected at the slip ring. Using also the data collected at the nearby wind turbines in the farm, it is possible to identify the incoming fault approximately one day before it occurs.
EN
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.
EN
The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.
PL
Wykrywanie uszkodzeń wewnętrznych wirnika o charakterze elektrycznym silników indukcyjnych dwuklatkowych jest zadaniem trudnym i wieloetapowym. Szczególnie ważna, w przypadku określenia zakresu remontu wirnika jest informacja, która klatka uległa uszkodzeniu. Celem niniejszego artykułu jest wskazanie skutecznej metody rozróżnienia uszkodzeń obwodów klatki rozruchowej od klatki pracy w stanie ustalonym z niesymetriami rezystancyjnymi prętów lub pierścieni zwierających. W pracy rozważono silnik z dwoma najczęściej występującymi konstrukcjami wirnika ze wspólnym i osobnymi pierścieniami zwierającymi obwody klatek. Przeprowadzono analizy diagnostyczne dla prototypowego silnika indukcyjnego dwuklatkowego małej mocy, w przypadku którego zarejestrowano sygnały prądów stojana podczas pomiarów w laboratorium. Wyniki pomiarów porównano z obliczeniami uzyskanymi z modelu polowego silnika indukcyjnego dwuklatkowego z niesymetrycznymi obwodami wirnika.
EN
Detecting internal damages of an electrical rotor of double cage induction motors is a difficult and multistage task. Particularly important, in the case of determining the scope of repair of the rotor, is the information that would allow one to determine which cage of the rotor has been damaged. The purpose of the article is to indicate an effective method of distinguishing failures of the starting cage from a working cage, with the resistive asymmetries of bars or rings. In the article the authors considered a motor with two most common rotor designs, i.e. with the common and separate rings the circuits of cages, respectively. Diagnostic analysis was performed for a double cage low-power induction motor prototype for which stator current signals were recorded during the measurements in the laboratory.The results of the measurements were compared against the calculations obtained using the finite element model (FEM).
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