Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona,... [more]

Nowadays, we can observe a growing interest in passive buildings due to global climate change, environmental concerns, and growing energy costs. However, developing a passive building is associated with meeting many Passive House requirements, which results in their increased complexity as well as many challenges and risks which could threaten the successful completion of the project. Risk management is a key tool enabling meeting today’s challenging passive house project’s demands connected with quality, costs, deadlines, and legal issues. In this paper, a new model of risk management dedicated for passive buildings based is proposed, in which a novel Fuzzy Fault Tree integrated with risk response matrix was developed. We proposed 171 risk remediation strategies for all 16 recognized risks in passive buildings projects. We show how to apply the proposed model in practice on one passive building example. Thanks to applying the proposed risk management model an effective reduction of th... [more]

The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data... [more]

Stringent environmental regulations and efforts to improve the shipping operations sustainability have resulted in designing and employing more complex configurations for the ship power plants systems and the implementation of digitalised functionalities. Due to these systems complexity, critical situations arising from the components and subsystem failures, which may lead to accidents, require timely detection and mitigation. This study aims at enhancing the safety of ship complex systems and their operation by developing the concept of an integrated monitoring safety system that employs existing safety models and data fusion from shipboard sensors. Detailed Fault Trees that model the blackout top event, representing the sailing modes of a cruise ship and the operating modes of its plant, are employed. Shipboard sensors’ measurements acquired by the cruise ship alarm and monitoring system are integrated with these Fault Trees to account for the acquired shipboard information on the in... [more]

Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistical... [more]

While systems in the automotive industry have become increasingly complex, the related processes require comprehensive testing to be carried out at lower levels of a system. Nevertheless, the final safety validation is still required to be carried out at the system level by automotive standards like ISO 26262. Using its guidelines for the development of automated vehicles and applying them for field operation tests has been proven to be economically unfeasible. The concept of a modular safety approval provides the opportunity to reduce the testing effort after updates and for a broader set of vehicle variants. In this paper, we present insufficiencies that occur on lower levels of hierarchy compared to the system level. Using a completely new approach, we show that errors arise due to faulty decomposition processes wherein, e.g., functions, test scenarios, risks, or requirements of a system are decomposed to the module level. Thus, we identify three main categories of errors: insuffici... [more]

Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faul... [more]

Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and measured vibration acceleration data. We introduce an approach that combines sequential model-based optimization and the nested cross-validation procedure to provide a reliable estimation of the classifiers’ generalization performance. These methods have not been combined earlier in this context. Automation of selected parts of the modeling procedure is studied with the measured data. We compare the performance of logistic regression and CatBoost models using the fast Fourier-transformed signals or their extracted statistical features as the input data. We develop a technique to use domain knowledge to extract features from specific frequency ranges of the fast Fourier-transformed sign... [more]

Background: Specialist literature indicates a large share of the human factor among the causes of failure of technical systems at the level of 70 to 90%, which depends on the sector studied. The collective water supply system is an anthropotechnical system, i.e., it is a complex connection between man and the technical system resulting from the deliberate influence of man on the technical system. Methods: The work presents an assessment of operator reliability of a selected water treatment process based on the fault tree analysis (FTA). Elementary events are determined by the operator’s error probability. Results: A failure tree was prepared for the peak event of the filter station failure, resulting from an operator’s error during the filter washing procedure. The probability of a peak event occurring is 0.0580. Conclusions: The developed fault tree allows for the identification of elementary events leading to an emergency event. The operator fulfills its task of maintaining the conti... [more]

This article presents a new method for the classification of machine failures using an example of selected generating sets. Measurements and an analysis of the electrical parameters, such as the phase-to-phase voltages at the terminals of a synchronous generator, armature current, and voltage and excitation current of a synchronous generator, are the basis for determining the failure symptoms. The existing energy quality coefficients are adopted as symptoms for the assessment of failures in the monitored generating set. We assume in this method that the description of the input−output relationship is in the form of a black box and use the binary diagnostics matrix (BDM) to investigate the failure−symptom relationships between the inputs (intentional failures) and outputs (failures symptoms = fault-sensitive power quality (PQ) coefficients). The method presented in this article enables the detection and classification of both electrical damage in a synchronous generator and mechanical d... [more]

Electrochemical low-cost sensors, suitable for the monitoring of different air quality parameters such as carbon monoxide or nitrogen dioxide levels, are viable tools for creating affordable handheld devices for short-term or dense air quality monitoring networks for long-term measurements and IoT applications. However, most devices that utilize such sensors are based on proprietary hardware and software and, therefore, do not offer users the ability to replace sensors or interact with the hardware, software, and data in a meaningful way. Initiatives that focus on an open framework for air quality monitoring, such as the AirSensEUR project, offer competitive open source alternatives. In this study, we examined the feasibility of the application of such devices. Five AirSensEUR units equipped with chemical sensors were placed next to a reference air quality measuring station in Vienna, Austria. During co-location, concentrations of 0.20 ± 0.06 ppm, 7.14 ± 8.66 ppb, and 17.58 ± 9.90 ppb... [more]

The increasing scale of industrial processes has significantly motivated the development of data-driven fault detection and diagnosis techniques. The selection of representative fault-free modeling data from operation history is an important prerequisite to establishing a long-term effective process monitoring model. However, industrial data are characterized by a high dimension and multimode, and are also contaminated with both outliers and frequent random disturbances, making automatic modeling data selection a great challenge in industrial applications. In this work, an information entropy-based automatic selection strategy for modeling data is proposed, based on which a general real-time process monitoring framework is developed for a large-scale industrial methanol to olefin unit with multiple operating conditions. Modeling data representing normal operating conditions are automatically selected with only a few manually defined normal samples. A long-term effective process monitor... [more]

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges... [more]

The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely and reliable response decisions. Because practical industrial processes have multimodal operating conditions, the data distributions of process variables are different. The different data distributions may cause the fault detection model to be invalid. In addition, the fault diagnosis model cannot find the correct root cause variable of system failure by only identifying abnormal variables. There are correlations between the trend states of the process variables. If we do not consider these correlations, this may result in an incorrect fault root cause. Therefore, multimodal industrial process monitoring is a tough issue. In this paper, we propose a three-step framework for multimodal industrial process monitoring. The framework aims for multimodal industrial processes to detect the faulty status timely and then find the correct root variab... [more]

The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault ty... [more]

Motor diagnostics is an important subject for consideration. Electric motors of different types are present in a multitude of object, from consumer goods through everyday use devices to specialized equipment. Diagnostic assessment of motors using acoustic signals is an interesting field, as microphones are present everywhere and are relatively easy sensors to process. In this paper, we analyze acoustic signals for the purpose of motor diagnostics using functional data analysis. We represent the spectrum (FFT) of the acoustic signals on a B-Spline basis and construct a classifier based on that representation. The results are promising, especially for binary classifiers, while multiclass (softmax regression) shows more sensitivity to dataset size. In particular, we show that while we are able to obtain almost perfect classification for binary cases, multiclass classifiers can struggle depending on the training/testing split. This is especially visible for determining the number of broken... [more]

Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters’ control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time−frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the... [more]

In the modern induction motor (IM) drive system, the fault-tolerant control (FTC) solution is becoming more and more popular. This approach significantly increases the security of the system. To choose the best control strategy, fault detection (FD) and fault classification (FC) methods are required. Current sensors (CS) are one of the measuring devices that can be damaged, which in the case of the drive system with IM precludes the correct operation of vector control structures. Due to the need to ensure current feedback and the operation of flux estimators, it is necessary to immediately compensate for the detected damage and classify its type. In the case of the IM drives, there are individual suggestions regarding methods of classifying the type of CS damage during drive operation. This article proposes the use of the classical multilayer perceptron (MLP) neural network to implement the CS neural fault classifier. The online work of this classifier was coordinated with the active F... [more]

The paper deals with the problem of accurate measuring techniques and experimental research methods for performance evaluation of direct contact jet-type flow condensers. The nominal conditions and range of temperature, pressure and flow rate in all characteristic points of novel test rig installation were calculated using the developed model. Next, the devices for measurement of temperature, pressure and flow rate in a novel test rig designed for testing the two-phase flow spray ejector condensers system (SEC) were studied. The SEC can find application in gas power cycles as the device dedicated to condensing steam in exhaust gases without decreasing or even increasing exhaust gas pressure. The paper presents the design assumptions of the test rig, its layout and results of simulations of characteristic points using developed test rig models. Based on the initial thermal and flow conditions, the main assumptions for thermal and flow process monitoring were formulated. Then, the discus... [more]

In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the... [more]

Recently, the concept of the microgrid (MG) has been developed to assist the penetration of large numbers of distributed energy resources (DERs) into distribution networks. However, the integration of DERs in the form of MGs disturbs the operating codes of traditional distribution networks. Consequently, traditional protection strategies cannot be applied to MG against short-circuit faults. This paper presents a novel intelligent protection strategy (NIPS) for MGs based on empirical wavelet transform (EWT) and long short-term memory (LSTM) networks. In the proposed NIPS, firstly, the three-phase current signals measured by protective relays are decomposed into empirical modes (EMs). Then, various statistical features are extracted from the obtained EMs. Afterwards, the extracted features along with the three-phase current measurement are input to three different LSTM network to obtain exact fault type, phase, and location information. Finally, a trip signal based on the obtained fault... [more]

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve th... [more]

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability an... [more]

The paper presents the Fractional Fourier Transform-Singular Value Decomposition (FRFT-SVD) method for the localization of various power system faults in a 200 km long, 500 kV Egyptian transmission line using sent end-line current signals. Transient simulations are carried out using Alternating Transient Program/Electromagnetic Transient Program (ATP-EMTP), and the outcomes are then examined in MATLAB to carry out a sensitivity analysis against measurement noises, sampling frequency, and fault characteristics. The proposed work employs current fault signals of five distinct kinds at nineteen intermediate points throughout the length of the line. The approach utilized to construct the localizer model is FRFT-SVD. It is much more effortless, precise, and effective. The FRFT-SVD is utilized in this technique to calculate 19 sets of indices of the greatest S value throughout the length of the line. The FRFT-SVD localizer model is also designed to be realistic, with power system noise corru... [more]

Electrodes for resistance spot welding inevitably wear out. In order to extend their service life, the tip-dressing process restores their original geometry. So far, however, the point in time for tip-dressing is mainly based on experience and not on process data. Therefore, this study aims to evaluate the in-situ or inline wear during the welding process without using additional sensors, and to base the timing for tip-dressing on continuous process monitoring, extending electrode life even further. Under laboratory conditions, electrode wear is analyzed by topographical measurements deepening the knowledge of the known main wear modes of resistance-spot-welding electrodes, mushrooming and plateau forming, and characterizing an electrode length delta over the number of spot welds. In general, electrode wear results in deformation of the electrode contact area, which influences process parameters and thereby weld quality. The conducted tests show correlation between this deformed contac... [more]