This study aimed to assess and compare the quality of groundwater in the city of Al-Marj in Libya with the international standard guidelines for drinking water recommended by the World Health Organisation. An evaluation of the groundwater wells in the study area was conducted. Standard techniques, such as Minitab (v. 16) and ArcGIS (v.10.2), were used for the analytics of the physicochemical and biological parameters of the groundwater samples. An assessment of the calculation of groundwater quality was conducted on the basis of temperature, pH, turbidity, electrical conductivity, total dissolved solids, chloride, sulphate, bicarbonate, total hardness, calcium, potassium, magnesium, ammonia, ammoniacal nitrogen, nitrate, sodium, copper, iron, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, total suspended solids, Escherichia coli and total coliform bacteria. Results indicated that most groundwater wells in the study area display a higher concentration of several pa... [more]

During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target... [more]

In recent years microfluidics and lab-on-a-chip havecome to the forefront in diagnostics and detection [...]

The production of light olefins by selective steam cracking is an energy-intensive process, and ethylene and propylene refrigeration cycles are key parts of it. The objective of this study was to identify opportunities for energy savings in an ethylene refrigeration cycle through an exergetic analysis. Two main causes of lower operational efficiency were identified: (1) Lower polytropic efficiency of the refrigerant compressor and (2) operating with the compressor mini-flow valve open to ensure reliability. The evaluation showed that the amount of irreversibilities generated by the cycle in operation is 22% higher than that predicted by the original design, which represents a 14% lower exergy efficiency. There is a potential savings of 0.20 MW in the cycle’s energy consumption with the implementation of the following improvements: recover refrigerant compressor efficiency by performing maintenance on the equipment and optimize the flow distribution between the recycle valve, the level... [more]

In this article, a type of diagnostic tool for an asynchronous motor powered from a frequency converter is proposed. An all-purpose, effective, and simple method for asynchronous motor monitoring is used. This method includes a single vibration measuring device fixed on the motor’s housing to detect faults such as worn-out or broken bearings, shaft misalignment, defective motor support, lost phase to the stator, and short circuit in one of the phase windings in the stator. The gathered vibration data are then standardized and continuous wavelet transform (CWT) is applied for feature extraction. Using morl wavelets, the algorithm is applied to all the datasets in the research and resulting scalograms are then fed to a complex deep convolutional neural network (CNN). Training and testing are done using separate datasets. The resulting model could successfully classify all the defects at an excellent rate and even separate mechanical faults from electrical ones. The best performing model... [more]

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is... [more]

The paper describes a newly proposed combination of the two existing Duval Pentagons method utilized for the identification of mineral oil-insulated transformers. The aim of the combination is to facilitate automatic fault identification through computer programs, and at the same time, apply the full capability of both original Pentagons, now reduced to a single geometry. The thorough classification of a given fault (say, of the electrical or thermal kind), employing individual Pentagons 1 and 2, as originally defined, involves a complex geometrical problem that requires the build-up of a convoluted geometry (a regular Pentagon whose axes represent each of five possible combustible gases) to be constructed using computer language code and programming, followed by the logical localization of the geometrical centroid of an irregular pentagon, formed by the partial contribution of individual combustibles, inside two similar structures (Pentagons 1 and 2) that, nonetheless, have different... [more]

This paper deals with a comparative study of two phasor estimators based on the least square (LS) and the linear Kalman filter (KF) methods, while assuming that the fundamental frequency is unknown. To solve this issue, the maximum likelihood technique is used with an iterative Newton−Raphson-based algorithm that allows minimizing the likelihood function. Both least square (LSE) and Kalman filter estimators (KFE) are evaluated using simulated and real power system events data. The obtained results clearly show that the LS-based technique yields the highest statistical performance and has a lower computation complexity.

The increasing cultivation of microalgae in photobioreactors warrants efficient and non-invasive methods to quantify biomass density in real time. Nephelometric turbidity assessment, a method that measures light scatter by particles in suspension, was introduced already several decades ago but was only recently validated as a high-throughput tool to monitor microalgae biomass. The light scatter depends on the density of the suspended particles as well as on their physical properties, but so far there are hardly any accounts on how nephelometric assessment relates to classic methods such as dry weight and spectrophotometric measurement across a broad biomass density range for different microalgae species. Here, we monitored biomass density online and in real time during the semi-continuous cultivation of three commercial microalgae species Chloromonas typhlos, Microchloropsis gaditana and Porphyridium purpureum in pilot-scale photobioreactors, and relate nephelometric turbidity to dry w... [more]

Fault tree analysis (FTA) is frequently applied to deductively evaluate the safety systems of complex engineering systems such as chemical industries or nuclear facilities. To perform this analysis, generic data are commonly used due to the limitation of historical failure data of the system being evaluated. However, generic data have a degree of uncertainty and hence cannot represent the system’s actual performance. In addition, generic data are not applicable to older components due to the aging process, which obviously degrades the reliability of those components. To deal with this limitation, another safety analysis method, called fuzzy fault tree analysis (FFTA), has been proposed. The purpose of this study is to apply FFTA to evaluate the performance of the primary cooling systems of G.A. Siwabessy Multipurpose Reactor (RSG-GAS). RSG-GAS is a research reactor, which belongs to the National Nuclear Energy Agency of Indonesia (BATAN). Expert justifications were used to evaluate the... [more]

In a nuclear power plant, the consequences of a multi-unit event occurring concurrently are more serious than those of a single-unit event. The first step in the probabilistic safety analysis of multi-units is to analyze the initiating events and calculate the frequency of initiating events for simultaneous events of multiple units. The difficulty in using the fault tree model is that the known data are all frequency data from a single unit and cannot be logically multiplied. In this paper, taking a dual unit as an example, we used the formula to convert the probability of failure of the second unit within 72 h and then build a fault tree model. After analyzing the results of the dual unit, the most frequent cut set was the common cause of failure of the main transformer and of the switching failure of the main and auxiliary external power. The final calculation of the frequency of simultaneous loss of off-site power events for the dual units within 72 h was 3.22 × 10−4/year. After com... [more]

Solids holdup as one of the main parameters in characterizing the performance of fluidized bed reactors is widely concerned. With its development and improvement, visualization technology has been applied in fluidization because of its little disturbance to the flow. In this study, four types of particles with different properties are tested in a narrow rectangular fluidized bed equipped with a high-speed video camera. Calibration curves of these different types of particles are achieved by correlating the grayscale of the digital images with the corresponding solids holdup. These calibration curves are further applied to obtain the average solids holdup across the sectional area and local solids holdup from the center towards the wall in both a gas-solids turbulent fluidized bed and a circulating fluidized bed to verify the results. The calibration method works well for solids holdup of different types of particles in both dense and dilute fluidization systems. This method is importan... [more]

The issue of detecting and locating defects generating partial discharges (PDs) is very important for the proper functioning of power grids. Despite the existence of many localization methods, both very large and relatively small objects are still a challenge due to the problem of obtaining the required measurement accuracy. This article presents the idea of the method of PD localization in small objects of simple structure with the use of a system of four capacitive probes. Based on the relative difference in the amplitudes of the signals recorded by the pair of capacitive sensors and considering their distance characteristics, it is possible to determine the place where the PD pulses are generated. In the example of measurements made on a support insulator, it was shown that the location of a defect using the proposed method allows for an indication accuracy of up to 0.5 cm.

Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribu... [more]

The possibility for spatial and temporal monitoring of environmental, chemical or thermal processes is of high importance for their better understanding thus control and optimization. Therefore, measurement methods that enable such opportunities might be especially valuable for researchers and process engineers. For this reason, in this paper the novel Electrical Impedance Tomography system is proposed that enables the visualization of the processes in which the electrical conductivity of material is changing. The proposed EIT system is based mostly on general purpose equipment. It consists of three laboratory-grade devices: a signal generator, a switching device and a data acquisition card for voltage measurement. In addition to those devices, the current source was constructed to complete the system. The EIT system was designed to have the ability of sourcing the current of frequency up to 250 kHz. A set of validation experiments were carried out to verify the EIT system accuracy. Th... [more]

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]