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1

Improving the exploitation efficiency of cooling equipment by monitoring operational parameter

Pędzik Robert Mirosław

Diagnostyka

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2024

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Vol. 25, No. 2

art. no. 2024205

EN

Cooling equipment is widely used in industry, commerce and households. Due to their widespread use, they are responsible for the consumption of a significant amount of electricity. They are subject to degradation and various types of damage. Most often, their energy efficiency decreases and electricity consumption increases. Practice shows that even a specialist service is unable to diagnose damage at an early stage of its development. The paper presents a comparison of continuous monitoring of the temperature of the cooling chamber as a utility standard, with constant monitoring of the temperature of the cooling chamber and electricity consumption of a professional refrigeration cabinet with a built-in condensing unit. The comparative analysis was intended to confirm the thesis about unconscious waste resulting from assessing the correct operation of the device based on limited information. The experiment showed an increase in daily electricity consumption on average by over 30% during the period of unconscious exploitation of the device in a state of failure and an increase in daily electricity consumption on average above 300% during the period of conscious exploitation of the device in a state of failure, but still at an acceptable level of temperature of cooling chamber.

2

Fault detection and diagnosis of squirrel cage asynchronous motor using a new COMSOL multiphysics software 6.0.

Djaidir Benrabeh, Nail Bachir, Rouibah Abdelkader

Diagnostyka

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2024

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Vol. 25, No. 1

art. no. 2024102

EN

In this paper, the diagnosis of faults in squirrel cage asynchronous motor and experimental analysis process are presented. Currently there are several simulation tools, that lets users analyze and interpret the behavior of their devices. Based on this, there is a lot of researches that is working on developing models, to detect and classify 3-phase asynchronous motor faults, significantly in the early stages. This work proposed design and experimental analysis established in Comsol Multiphysics 6.0 , which implements finite element analysis software (FEM) for detecting and diagnosing broken bar rotors of this types motors and its practical application. In this case, the post processor of the COMSOL-Multiphysics makes it possible to visualize in 2D the various magnetic and mechanical quantities. Through the curves of the magnetic flux density and analysis distribution of the field with magnetic induction lines, we can draw some conclusions, where we proposed an strategy, for detecting and diagnosing faults consistent with the structure of the software.

3

Detection and identification of fault in transmission lines based on ANN

Abdullah Ahmed G., Ibrahim Mohammad A., Ibrahim Waseem Kh.

Diagnostyka

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2024

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Vol. 25, No. 2

art. no. 2024206

EN

Along with power transmission lines' efficiency, another crucial factor in electrical power transmission networks is reliability, which guarantees power transmission stability. One of the crucial and essential tasks for maintaining the continuity and stability of power transmission in transmission networks Capacity without any significant failures is identifying errors and malfunctions in power transmission lines as soon as possible. The goal of this article is to develop and apply ANN technology to overcome the obstacles faced by the electrical power transmission network. In order for the ANN to learn useful patterns and features from raw current measurements, pre-processing and feature extraction techniques are used during the training process. Real-time applications can benefit from the ANN's architecture, which is optimized for high accuracy, quick response times, and scalability. To validate the performance of the ANN-based fault detection system, extensive simulations are conducted using data from different transmission line scenarios, including various fault types that short-circuit. The results demonstrate the capability of the ANN model to accurately detect and classify faults, as well as disconnect the power grid after detect any fault. The results showed the accuracy and high speed of the proposed method using a neural network compared to traditional methods.

4

Wykrywanie zdarzeń awaryjnych w silnikach indukcyjnych na podstawie analizy danych z rejestratorów zakłóceń

Sajewicz Dariusz, Łaguna Wojciech, Chmielak Waldemar

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

207--210

PL

Celem artykułu jest wykazanie skuteczności nowo opracowanych metod detekcji uszkodzeń opartych na analizie danych z rejestratorów zakłóceń. W trakcie prac badawczych wyekstrahowano najbardziej istotne cechy sygnałów prądów w dziedzinie częstotliwości. Pozyskane cechy stanowiły podstawę budowy probabilistycznego klasyfikatora zdarzeń awaryjnych. Detekcja uszkodzeń dotyczy wykrywania: pękniętych prętów wirnika i stopnia jego degradacji oraz awarii łożysk na wale silnika. Przeprowadzone badania potwierdzają wysoką skuteczność wykrywania uszkodzeń we wszystkich rozpatrywanych obszarach.

EN

The aim of this paper is to demonstrate the effectiveness of developed fault detection methods based on the analysis of data from fault recorders. During the research work, the most significant features of current signals in the frequency domain were extracted. The extracted features provided the base for building a probabilistic classifier of fault incidents. The fault detection concerned the detection of cracked rotor cages and the degree of its degradation as well as the failure of bearings on the motor shaft. The conducted research confirms the high efficiency of detection faults in all areas concerned.

5

Fault detection and diagnosis of air-conditioning system using long short-term memory recurrent neural network

Sulaiman Noor Asyikin, Menanti Nur Amalina Sabal, Yusop Azdiana Md, Zainudin Muhammad Noorazlan Shah, Yatim Norhidayah Mohamad, Razak Norazlina Abd, Abdullah Md Pauzi

Przegląd Elektrotechniczny

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2023

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R. 99, nr 9

113--117

EN

In this project, a fault detection and diagnosis (FDD) system was developed using Long Short-Term Memory Recurrent Neural Network (LSTM RNN), to detect and classify six common faults in a centralised chilled water air conditioning system. Datasets from a lab-scale centralised chilled water air conditioning system were used in the developed model. Results showed that the classifier model demonstrated a classification accuracy of over 99.3% for all six classes.

PL

W ramach tego projektu opracowano system wykrywania i diagnozowania usterek (FDD) z wykorzystaniem powtarzającej się sieci neuronowej długookresowej pamięci (LSTM RNN) w celu wykrycia i sklasyfikowania sześciu powszechnych usterek w scentralizowanym systemie klimatyzacji wody lodowej. W opracowanym modelu wykorzystano zestawy danych ze scentralizowanego systemu klimatyzacji wody lodowej w skali laboratoryjnej. Wyniki pokazały, że model klasyfikatora wykazał dokładność klasyfikacji na poziomie ponad 99,3% dla wszystkich sześciu klas.

6

Detekcja uszkodzeń czujników prądu stojana w układach napędowych z silnikami PMSM

Jankowska Kamila, Dybkowski Mateusz

Przegląd Elektrotechniczny

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2023

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R. 99, nr 3

16--22

PL

W artykule opisano detektor uszkodzeń czujników prądu stojana w układzie napędowym z silnikiem synchronicznym z magnesami trwałymi (PMSM). Rozwiązanie to zostało wcześniej opisane dla silników indukcyjnych. Mechanizm detekcji opiera się na tzw. markerach prądowych. Zastosowanie markerów umożliwia zarówno detekcję uszkodzenia jak i lokalizację uszkodzonej fazy. Działanie systemu opiera się wyłącznie na analizie pomiarów z czujników prądu i nie wymaga dodatkowych informacji o napędzie. W pracy skupiono się na analizie badań eksperymentalnych dla opracowanego detektora uszkodzeń czujnika prądu dla różnych warunków pracy napędu PMSM sterowanego metodą DFOC.

EN

The article describes a fault detector of stator current sensors in a drive system with a permanent magnet synchronous motor (PMSM). This solution was previously described for induction motors. The detection mechanism is based on the so-called current markers. The use of markers enables both damage detection and the location of the damaged phase. The operation of the system is based solely on the analysis of measurements from current sensors and does not require additional information about the drive. The work focuses on the analysis of experimental studies for the developed current sensor fault detector for various operating conditions of vector controlled PMSM drive.

7

Feasibility study of a rail vehicle damper fault detection by artificial neural networks

Melnik Rafał, Koziak Seweryn, Dižo Ján, Kuźmierowski Tomasz, Piotrowska Ewa

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 1

art. no. 5

EN

The aim of the study was to investigate rail vehicle dynamics under primary suspension dampers faults and explore possibility of its detection by means of artificial neural networks. For these purposes two types of analysis were carried out: preliminary analysis of 1 DOF rail vehicle model and a second one - a passenger coach benchmark model was tested in multibody simulation software - MSC.Adams with use of VI-Rail package. Acceleration signals obtained from the latter analysis served as an input data into the artificial neural network (ANN). ANNs of different number of hidden layers were capable of detecting faults for the trained suspension fault cases, however, achieved accuracy was below 63% at the best. These results can be considered satisfactory considering the complexity of dynamic phenomena occurring in the vibration system of a rail vehicle.

8

A Semi-Supervised Siamese Network for Complex Aircraft System Fault Detection with Limited Labeled Fault Samples

Xinyun Zhu, Sun Jianzhong, Hu Hanchun, Li Chunhua

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 4

art. no. 174382

EN

Health monitoring and fault detection of complex aircraft systems are paramount for ensuring reliable and efficient operation. The availability of monitoring data from modern aircraft onboard sensors provides a wealth of big data for developing deep learning-based fault detection methods. However, aircraft onboard systems typically have limited labeled fault samples and large amounts of unlabeled data. To better utilize the information contained in limited labeled fault samples, a deep learning-based semi-supervisedfault detection method is proposed, which leverages a small number of labeled fault samples to enhance its performance. A novel sample pairing strategy is introduced to improve algorithm performance by iteratively utilizing fault samples. A comprehensive loss function is employed to accurately reconstruct normal samples and effectively separate fault samples. The results of a case study using real data from a commercial aircraft fleet demonstrate the superiority of the proposed method over existing techniques, with improvements of approximately 16.7% in AP, 9.5% in AUC, and 19.2% in F1 score. Ablation studies confirm that performance can be further improved by incorporating additional labeled fault samples during training. Furthermore, the algorithm demonstrates good generalization ability.

9

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Zemali Zakaria, Cherroun Lakhmissi, Hadroug Nadji, Nadour Mohamed, Hafaifa Ahmed

Diagnostyka

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2023

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Vol. 24, No. 1

art. no. 2022110

EN

This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.

10

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Rahmoune Ben Mohamed, Iratni Abdelhamid, Amari Amel Sabrine, Hafaifa Ahmed, Colak Ilhami

Diagnostyka

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2023

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Vol. 24, No. 3

art. no. 2023303

EN

Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.

11

Fabrication of a test rig for gearbox fault simulation and diagnosis

Dubaish Asaad Abdulhussein, Jaber Alaa Abdulhady

Diagnostyka

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2023

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Vol. 24, No. 2

art. no. 2023204

EN

Gearboxes are one of the most important and widely exposed to different types of faults in machines. Therefore, manufacturers and researchers have made significant efforts to develop different fault detection and diagnostic approaches for gearboxes. However, many research foundations, such as universities, are currently working on developing different gearbox test rigs to understand the failure mechanisms in gearboxes. As a result, in this article, a gearbox testing rig was proposed and fabricated to evaluate gear performance under lowspeed working conditions. It describes the primary mechanical apparatus and the measurement tools used during the experimental analysis of a multistage gearbox transmission system. The data-gathering equipment used to acquire the observed vibration data is also discussed. LabVIEW software was used to build a data acquisition platform using an accelerometer and a NI DAQ device. Then different vibration tests were conducted under different operating conditions, when the gearbox was healthy and then faulty, on this test rig, and the gathered vibration data were analyzed based on time domain signal analysis. The preliminary results are promising and open the horizon for simulating different gearbox test scenarios.

12

Siamese neural networks on the trail of similarity in bugs in 5G mobile network base stations

Zarębski Sebastian, Kuzmich Aliaksandr, Sitko Sebastian, Rusek Krzysztof, Chołda Piotr

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2022

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nr 4

198--201

EN

To improve the R&D process, by reducing duplicated bug tickets, we used an idea of composing BERT encoder as Siamese network to create a system for finding similar existing tickets. We proposed several different methods of generating artificial ticket pairs, to augment the training set. Two phases of training were conducted. The first showed that only and approximate 9% pairs were correctly identified as certainly similar. Only 48% of the test samples are found to be pairs of similar tickets. With the fine-tuning we improved that result up to 81%, proving the concept to be viable for further improvements.

13

Detection of fault events in medium voltage grid based on analysis of data from fault recorders

Sajewicz Dariusz, Wójcik Augustyn, Zegadło Piotr

Przegląd Elektrotechniczny

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2022

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R. 98, nr 3

131--135

EN

The aim of this paper is to demonstrate the effectiveness of newly developed fault detection methods based on a simple statistical approach encompassing linear discriminant analysis and signal processing. Fault prediction relates to the detection of: the type of operation of the medium voltage network, leakage (damaged insulator in the line string) and a measure of the distance of ground fault in an unbranched line, in a branched line and on its branches. The conducted research confirms the high efficiency of detection faults in all areas concerned.

PL

Celem pracy jest wykazanie skuteczności nowo opracowanych metod detekcji uszkodzeń opartych na prostym podejściu statystycznym obejmującym liniową analizę dyskryminacyjną i przetwarzanie sygnałów. Przeprowadzone badania potwierdzają wysoką skuteczność wykrywania uszkodzeń we wszystkich rozpatrywanych obszarach.

14

Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization

Barszcz Tomasz, Zabaryłło Mateusz

Metrology and Measurement Systems

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2022

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Vol. 29, nr 1

175--190

EN

Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.

15

Fault-tolerant tracking control for a non-linear twin-rotor system under ellipsoidal bounding

Kukurowski Norbert, Mrugalski Marcin, Pazera Marcin, Witczak Marcin

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

171--183

EN

A novel fault-tolerant tracking control scheme based on an adaptive robust observer for non-linear systems is proposed. Additionally, it is presumed that the non-linear system may be faulty, i.e., affected by actuator and sensor faults along with the disturbances, simultaneously. Accordingly, the stability of the robust observer as well as the fault-tolerant tracking controller is achieved by using the ℋ∞ approach. Furthermore, unknown actuator and sensor faults and states are bounded by the uncertainty intervals for estimation quality assessment as well as reliable fault diagnosis. This means that narrow intervals accompany better estimation quality. Thus, to cope with the above difficulty, it is assumed that the disturbances are over-bounded by an ellipsoid. Consequently, the performance and correctness of the proposed fault-tolerant tracking control scheme are verified by using a non-linear twin-rotor aerodynamical laboratory system.

16

A graph theory-based approach to the description of the process and the diagnostic system

Kościelny Jan Maciej, Bartyś Michał, Syfert Michał, Sztyber Anna

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

213--227

EN

The paper proposes an original, comprehensive, and methodically consistent graph theory-based approach to the description of the diagnosed process and the diagnosing system. The main baseline of the presented approach is in the dichotomous approach to diagnosing. It involves a separate description of both the process and the diagnostic system. This approach reflects the practice of designing implementable diagnostic systems. Thus, it can be seen as a proposal of a new, alternative, and, at the same time, flexible design procedure with great potential for applications. The primary motivation behind it was an attempt to circumvent the numerous limitations of well-known and well-established diagnosis approaches proposed by the communities working on fault detection and isolation (FDI) and artificial intelligence theories for diagnosis (DX). Accordingly, the paper identifies and provides an extensive discussion and a critical analysis of the existing limitations. Numerous examples and references to practical applications of the approach are indicated.

17

A data driven fault isolation method based on reference faulty situations with application to a nonlinear chemical process

Ragot José, Mourot Gilles, Kallas Maya

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 4

635--655

EN

The diagnosis of systems is one of the major steps in their control and its purpose is to determine the possible presence of dysfunctions, which affect the sensors and actuators associated with a system but also the internal components of the system itself. On the one hand, the diagnosis must therefore focus on the detection of a dysfunction and, on the other hand, on the physical localization of the dysfunction by specifying the component in a faulty situation, and then on its temporal localization. In this contribution, the emphasis is on the use of software redundancy applied to the detection of anomalies within the measurements collected in the system. The systems considered here are characterized by non-linear behaviours whose model is not known a priori. The proposed strategy therefore focuses on processing the data acquired on the system for which it is assumed that a healthy operating regime is known. Diagnostic procedures usually use this data corresponding to good operating regimes by comparing them with new situations that may contain faults. Our approach is fundamentally different in that the good functioning data allow us, by means of a non-linear prediction technique, to generate a lot of data that reflect all the faults under different excitation situations of the system. The database thus created characterizes the dysfunctions and then serves as a reference to be compared with real situations. This comparison, which then makes it possible to recognize the faulty situation, is based on a technique for evaluating the main angle between subspaces of system dysfunction situations. An important point of the discussion concerns the robustness and sensitivity of fault indicators. In particular, it is shown how, by non-linear combinations, it is possible to increase the size of these indicators in such a way as to facilitate the location of faults.

18

Intelligent fault diagnosis of power transmission line using fuzzy logic and artificial neural network

Touati Khaled Omer Mokhtar, Boudiaf Mohamed, Merzouk Imad, Hafaifa Ahmed

Diagnostyka

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2022

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Vol. 23, No. 4

art. no. 2022410

EN

In the industrial sector, transmission lines are an important part of the electrical grid. Thus it is important to protect it from all the different faults that may occur as soon as possible to supply the electric power continuously. This paper presents a modern solutions and a comparative study of fault detection and identification in electrical transmission lines using artificial neural network (ANN) compare to the fuzzy logic. Faults in transmission line of various types have been created using simulation model. An intelligent monitoring system (IFD: Intelligent Fault Diagnosis) was used at both ends of a 230 kV overhead transmission line, voltage and current measurements exploited as indicator data for this system. Both approaches were found to be robust, accurate and reliable to detect the fault when it occurs, to determine the fault type short circuit or opening of a power line (open circuit), to locate the fault and to determine which phase was faulted.

19

Effects of harmonics and voltage unbalance on the behavior of a five-phase permanent magnet synchronous machine

Kebir Ahmed Amine, Ayad Mouloud, Kamel Saoudi

Archives of Electrical Engineering

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2022

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Vol. 71, nr 2

471--488

EN

The development in industrial systems leads to the augmentation in the consumption of the power. Therefore, this development makes use of multiphase machines. The use of multiphase machines caused several problems and defects. Electrical energy is mainly distributed in a three-phase system to provide the electrical power necessary for the electrical engineering equipment and materials. The sinusoidal aspect of the required original voltage primarily preserves its essential qualities for transmitting useful power to terminal equipment. When the voltage waveform is no longer sinusoidal, perturbations are encountered, which generate malfunctions and overheating of the receivers and the equipment connected to the same electrical supply network. The main disturbing phenomena are harmonics, voltage fluctuations, voltage unbalances, electromagnetic fields, and electrostatic discharges. This present work aims to study the effects of harmonic pollution and voltage unbalance on the five-phase permanent magnet synchronous machine using spectrum current analysis and wavelet transform.

20

Optimized Approach of Feature Selection Based on Binary Genetic Algorithm in Classification of Induction Motor Faults

Le Truong-An

Annals of Computer Science and Information Systems

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2022

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Vol. 33

145--150

EN

In this paper, an effective model for detection and classification of multiple faults in induction motors is presented. It used S-transform method is used to analyze current signals measured from four different motors including a healthy motor, broken rotor bars, bearing damage, stator winding short-circuits fault. The feature set is extracted based on signal spectrum. With strong exploration capabilities in the search space, binary genetic algorithm (BGA) is proposed to select the optimal feature subset. As the classifier, the backpropagation neural network and support vector machine are used. The simulation results showed that the average accuracy of 100 trails is 98.3\% and the optimal feature subset equal to 36\% of total original features. That means the number of redundant features removed is 64\%. In conclusion, the proposed model combined with BGA reached highly effective in the classification of induction motor.