Wyniki wyszukiwania - Biblioteka Nauki

1

The possibilities of early fault detection of engines

100%

Filipczyk J.

Journal of KONES

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2013

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tom Vol. 20, No. 4

79--84

EN

The results of investigation of 1,9 TDI engine (285 Nm, 85 kW, type AJM without any modification) equipped with injection units supplied conventional diesel fuel (ON) or B100 fuel (RME) have been presented in article. Investigations have been realized at the engine speed of 2000 rpm and variable load within the range of 0 to 275 Nm. The pressure, temperature and heat release velocity runs have been subjected to analysis. Particular attention has been paid to the release of the heat used for the effective work and internal energy increase of the working medium (enthalpy) during combustion inside the engine cylinder versus the crank angle for both investigated fuels. It was found among the others that mentioned fuels differ in the heat release, heat velocity and the maximal combustion temperature, which for the B100 fuel is bigger than for the conventional diesel fuel. Bigger combustion dynamics of tested biofuel (compared with standard diesel fuel) results higher concentrations of Nitrogen Oxides NOx in exhaust gases. The easiest way is of course the use of the later start of fuel injection biofuels and/or increase the exhaust gas recirculation EGR. These treatments, however, result in a worsening of the energy performance of the engine. It was concluded also that the combustion of RME works properly at higher engine loads. Then reduce the negative difference between the combustion of biofuel (RME) and standard Diesel fuel.

2

Journal Bearing Fault Detection Based on Daubechies Wavelet

80%

Narendiranath B. T. , Himamshu H. S. , Prabin K. N. , Rama P. D. , Nishant C.

Archives of Acoustics

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2017

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tom Vol. 42, No. 3

401--414

EN

Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.

3

Multiple soft fault diagnosis of BJT circuits

80%

Tadeusiewicz M. , Hałgas S.

Metrology and Measurement Systems

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2014

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tom Vol. 21, nr 4

663--674

EN

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.

4

Multiple soft fault diagnosis of nonlinear circuits using the fault dictionary approach

80%

Hałgas S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2008

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tom Vol. 56, nr 1

53-57

EN

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.

5

Fault diagnosis in analog electronic circuits - the SVM approach

80%

Grzechca D. , Rutkowski J.

Metrology and Measurement Systems

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2009

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tom Vol. 16, nr 4

583-597

EN

In this paper, the application of the SVM (Support Vector Machine) algorithm has been used for diagnosis and tests of analog electronic circuits. The diagnosis procedure belongs to simulation-before-test techniques, where simulations of the circuit under test (CUT) are performed at the before-test stage. Two examples have been verified for parametric and catastrophic faults in the time domain, but the conclusion is driven with the use of assumed features. A fault-driven test (FDT) has been applied to a filter circuit and a specification-driven test (SDT) to a field-programmable analog array (FPAA). The SVM classifies features which are calculated from the time domain responses. Results obtained from the examples prove a high detection and localization level of circuit states with the use of the SVM classifier.

6

Rotating shaft fault prediction using convolutional neural network : a preliminary study

80%

Kolar D. , Lisjak D. , Pająk M.

Journal of KONES

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2019

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tom Vol. 26, No. 3

75--81

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.

7

Research on SDG fault diagnosis of ocean shipping boiler system based on fuzzy granular computing under data fusion

80%

Zhu Y. , Geng L.

Polish Maritime Research

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2018

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tom S 2

92--97

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.

8

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

80%

Manikandan P. , Geetha M.

Archives of Control Sciences

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2014

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tom Vol. 24, no. 3

271--287

EN

The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

9

Soft Fault Diagnosis in Analog Circuit Based on Fuzzy and Direction Vector

80%

Zhou L. , Shi Y. , Tang J. , Li Y.

Metrology and Measurement Systems

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2009

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tom Vol. 16, nr 1

61-75

EN

A basic circuit theory of fault diagnosis for analog circuits with parameter tolerance is proposed in this paper. The approach uses the direction vector of voltage increment in test nodes as a fault signature for predefined faults. A linear equation is built to locate a faulty element. On the condition that the component tolerances are taken into account, the concepts of direction vector and fuzzy analysis method are combined together to analyze a parametric fault. Examples illustrate the proposed approach and show its effectiveness.

10

Multiple soft fault diagnosis of nonlinear dc circuits considering component tolerances

80%

Tadeusiewicz M. , Hałgas S.

Metrology and Measurement Systems

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2011

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tom Vol. 18, nr 3

349-360

EN

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.

11

The identification of piston-cylinder clearance using time-frequency methods

80%

Flekiewicz M. , Fabiś P. , Madej H.

Journal of KONES

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2009

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tom Vol. 16, No. 4

93-103

EN

A fault recognition technique for the internal combustion engines using time-frequency representations of vibration signal has been presented in this paper. Engine block vibration results as a sum of many excitations mainly connected with engine speed and their intensity increases with the appearance of a fault or in case ofhigher engine elements wearing. In this paper an application of acceleration signals for the estimation of the influence of piston skirt clearance on diesel engine block vibrations has been described. Engine body accelerations registered for three simulated cases representing piston skirt clearance variations were an object of preliminary analysis. The presented procedures were applied to vibration and pressure signals acquired for a 0.5 dm3 Ruggerini, air cooled diesel engine. reciprocating machines are difficult to diagnose using traditional frequency domain techniques due to the fact they generale transient vibrations. In the experiments that were conducted Gabor Analysis and Adaptive Spectrogram has been chosen The Gabor spectrogram is a powerful tool for on-line monitoring and diagnosis of combustion process. There are important features of the vibration signal that are sensitive to the change of IC engine condition. For that reason the DWT transform was applied. Based on the results, authors propose detection and piston skirt clearance monitoring algorithm.

12

The typical faults of automotive engines and analysis for fault diagnosis possibility

80%

Filipczyk J. , Madej H.

Silniki Spalinowe

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2009

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tom R. 48, SC1

229--233

EN

This paper presents analysis of typical faults occurring in contemporary automotive engines and possibility of damage detection. The engines of 200 cars of the same make have been analysed as far as engine damage are concerned. Higher fuel consumption, more rapid wear of the engine components, damages in additional equipment of the engine have been examined as well. The possibilities of diagnosing the damages of mechanical and electrical systems of the engine have been carried out using electrical values measurements, vibroacoustics signals and exhaust gases analysis. 200 models of cars of 28 different makes have been tested regarding the possibilities of applying testers of on-board diagnosing systems and diagnosing damages.

PL

W artykule przedstawiono typowe uszkodzenia występujące we współczesnych silnikach samochodowych oraz możliwości ich diagnozowania. Przeanalizowano uszkodzenia silników w 200 samochodach jednej marki. Uszkodzenia obejmowały unieruchomienie silnika, zwiększenie zużycia paliwa, przyspieszone zużycie w węzłach tarcia i uszkodzenia związane z oprzyrządowaniem. Przeprowadzono badania możliwości diagnozowania uszkodzeń układów mechanicznych i elektrycznych silnika z wykorzystaniem pomiarów wielkości elektrycznych, sygnałów wibroakustycznych oraz analizy spalin. Dla 200 modeli samochodów osobowych 28 marek przeprowadzono badania w zakresie możliwości wykorzystania uniwersalnych testerów układów diagnostyki pokładowej do diagnozowania uszkodzeń silników.

13

Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy

80%

Ge L. , Fan W. , Xiao X. , Gan F. , Lai X. , Deng H. , Huang Q.

Engineering Transactions

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2022

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tom Vol. 70, iss. 1

23--51

EN

Due to the complex randomness and nonlinearity of rolling bearing vibration signal, it is challenging to extract fault features effectively. By analyzing the vibration mechanism of rolling bearing, it is found that the vibration signal of local damage defects of rolling bearing has the characteristics of periodic impact and amplitude modulation. The variational mode decomposition (VMD) algorithm has a good advantage in dealing with nonlinear and nonstationary signals and decomposing a signal into different modes. However, VMD has the problem of parameter selection, which directly affects the performance of VMD processing, and causes mode aliasing. Therefore, a rolling bearing fault diagnosis method based on improved VMD is proposed. A new fitness function combining differential evolution (DE) algorithm with gray wolf optimization (GWO) algorithm is proposed to form a new hybrid optimization algorithm, named DEGWO. The simulation results show that the improved VMD method based on DEGWO can adaptively remove the noise according to the characteristics of the signal and restore the original characteristics of the vibration signal. Finally, in order to verify the advantages of the research, the information entropy is extracted from the data of 1000 samples in the bearing database of Case Western Reserve University as the feature set, which is input into support vector machine (SVM) for fault diagnosis test. The results show that the diagnostic accuracy of this method is 96.5%, which effectively improved the accuracy of rolling bearing fault diagnosis.

14

Fault diagnosis of a water for injection system using enhanced structural isolation

80%

Laursen M. , Blanke M. , Düştegör D.

International Journal of Applied Mathematics and Computer Science

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2008

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tom 18

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nr 4

593-603

EN

A water for injection system supplies chilled sterile water as a solvent for pharmaceutical products. There are ultimate requirements for the quality of the sterile water, and the consequence of a fault in temperature or in flow control within the process may cause a loss of one or more batches of the production. Early diagnosis of faults is hence of considerable interest for this process. This study investigates the properties of multiple matchings with respect to isolability, and it suggests to explore the topologies of multiple use-modes for the process and to employ active techniques for fault isolation to enhance structural isolability of faults. The suggested methods are validated on a high-fidelity simulation of the process.

15

A key metric and its calculation models for a continuous diagnosis capability based on a dependency matrix

80%

Shi J.-Y. , Lin X.-G. , Shi M.

Metrology and Measurement Systems

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2012

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tom Vol. 19, nr 3

509-520

EN

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.

16

Fault diagnosis of me marine diesel engine fuel injector with novel IRCMDE method

80%

Shi Q. , Hu Y. , Yan G.

Polish Maritime Research

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2023

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tom nr 3

96--110

EN

As an important component of the fuel injection system, the fuel injector is crucial for ensuring the power, economy, and emissions for a whole ME (machine electronically-controlled) marine diesel engine. However, injectors are most prone to failures such as reduced pressure at the opening valve, clogged spray holes and worn needle valves, because of the harsh working conditions. The failure characteristics are non-stationary and non-linear. Therefore, to efficiently extract fault features, an improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed, which uses the energy distribution of sampling points as weights for coarse-grained calculation, then fast correlation-based filter (FCBF) and support vector machine (SVM) are used for feature selection and fault classification, respectively. The experimental results from a MAN B&W 6S35ME-B9 marine diesel engine show that the proposed algorithm can achieve 92.12% fault accuracy for injector faults, which is higher than multiscale dispersion entropy (MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale permutation entropy (MPE). Moreover, the experiment has also proved that, due to the double-walled structure of the high-pressure fuel pipe, the fuel injection pressure signal is more accurate than the vibration signal in reflecting the injector operating conditions.

17

State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

80%

Bouslimani S. , Meradi S. , Drid S. , Chrifi-Alaoui L. , Bezziane A.

Power Electronics and Drives

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2023

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tom Vol. 8 (43)

109-127

EN

This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.

18

Hierarchical multiscale fluctuation dispersion entropy for fuel injection system fault diagnosis

80%

Shi Q. , Hu Y. , Yan G.

Polish Maritime Research

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2023

|

tom nr 1

98--111

EN

Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.

19

Induction motor fault classification via entropy and column correlation features of 2D represented vibration data

80%

Basaran M. , Fidan M.

Eksploatacja i Niezawodność

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2021

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tom Vol. 23, no. 1

132--142

EN

Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.

20

Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes

80%

Kościelny J. M. , Syfert M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2014

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tom Vol. 62, nr 3

571--582

EN

The survey presents a selection of the methods of the fault detection and isolation suitable to be useful for the diagnostics of the complex, large scale industrial processes. The paper focuses on these methods that have appropriately high level of potential applicability in industrial practice. The novelty of the paper relies on the discussion of the dependency of the level of knowledge about diagnosed process and recommended diagnostic approaches. Appropriate recommendations were given in the convenient form of the table.