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Organizations involved in facility management (FM) can use building information modeling (BIM) as a knowledge repository to document evolving facility information and to support decisions made by the facility managers during the operational life of a facility. Despite ongoing advances in FM technologies, FM practices in most facilities are still labor intensive, time consuming and often rely on unreliable and outdated information. To address these shortcomings, the purpose of this study is to propose an automated approach that demonstrates the potential of using BIM to develop algorithms that automate decision-making for FM applications.

A BIM plug-in tool is developed that uses a fault detection and diagnostics (FDD) algorithm to automate the process of detecting malfunctioning heating, ventilation, and air conditioning (HVAC) equipment. The algorithm connects to a complaint ticket database and automates BIM to determine potentially damaged HVAC system components and develops a plan of action for the facility inspectors accordingly. The approach has been implemented as a case study in an operating facility to improve the process of HVAC system diagnosis and repair.

By implementing the proposed application in a case study, the authors found that automated BIM approaches such as the one developed in this study, can be highly beneficial in FM practices by increasing productivity and lowering costs associated with decision-making.

This study introduces an innovative approach that leverages BIM for automated fault detection in operational buildings. FM personnel in charge of HVAC inspection and repair can highly benefit from the proposed approach, as it eliminates the time required to locate HVAC equipment at fault manually.

The purpose of this paper is to present the topic of control computers diagnostics. They are part of an unmanned aerial vehicle (UAV) control system implemented in a modified version of MP-2 Czajka aircraft.

The algorithms were designed as a basic version of the diagnostic system. The system is open and will be developed.

First results show that the diagnostic system works properly. The system is easy for implementation and burdens the control computers only insignificantly.

The system presented can detect only computers out of work. In its present version, it cannot detect such errors as improper calculations of control signals. After first in-flight testing, the system will be further developed.

The diagnostic system is implemented in an UAV technology demonstrator.

The designed system is the part of an UAV control system, designed for ground observation. Such technology demonstrator and flying laboratory enable different type of research in the area of aviation.

The purpose of this paper is analysis of the sensorless control system of induction machine with broken rotor for diagnostic purposes. Increasing popularity of sensorless controlled variable speed drives requires research in area of reliability, range of stable operation, fault symptoms and application of diagnosis methods.

T transformation used for conversion of instantaneous rotor currents electrical circuit representation to space vector components is investigated to apply with closed‐loop modeling algorithm. Evaluation of the algorithm is based on analysis of asymmetry influence to the orthogonal and zero components of space vector representation. Multiscalar model of the machine and selected structures of state observers are used for sensorless control system synthesis. Proposed method of frequency characteristics calculation is used for state observers analysis in open‐loop operation.

New algorithm of applying the T transformation allows for closed‐loop and sensorless control system simulation with asymmetric machine due to broken rotor. Compensating effect of the closed‐loop control system with speed measurements and diagnosis information in control system variables are identified. Proposed frequency analysis of state observers is presented and applied. Variables with amplified characteristic frequency components related to rotor asymmetry are compared for selected structures of state observers and with closed‐loop and open‐loop operation. Method of improving the sensorless system stability is proposed.

In closed‐loop and sensorless control system rotor fault can be diagnosed by using PI output controllers variables. Compensating effect of mechanical variables sets limitation to specified diagnosis methods. Rotor asymmetry affects sensorless control system stability depending on estimator structure.

This paper concentrates upon sensorless control system operation with machine asymmetry and indicates rotor fault symptoms.

The developments of complex systems have increased the demand for condition monitoring techniques so as to maximize operational availability and safety while decreasing the costs. Signal analysis is one of the methods used to develop condition monitoring in order to extract important information contained in the sensory signals, which can be used for health assessment. However, extraction of such information from collected data in a practical working environment is always a great challenge as sensory signals are usually multi-dimensional and obscured by noise. The paper aims to discuss this issue.

This paper presents a method for trends extraction from multi-dimensional sensory data, which are then used for machinery health monitoring and maintenance needs. The proposed method is based on extracting successive features from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the source of the signals and the number of the extracted features. Finally, empirical mode decomposition (EMD) algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time.

The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and a second data set acquired form NASA repository.

The method showed that it is able to extract interesting signal trends which can be used for health monitoring and remaining useful life prediction.

No fault found (NFF) in maintenance has been a frequently observed problem in industrial sectors, but very few academic contributions are devoted to reviewing and summarizing the related research. Considering the growing interest of academicians in NFF during the last decade, there is a critical need to examine theme evolution in this field, most influential authors, contemporary practices, research gaps and proposed solutions.

A portfolio of 169 articles published between 1982 and 2020 was collected from the Scopus database and was systematically analyzed using a two-tier method. First, the evolution, current state of literature and research clusters are identified using bibliometric techniques. Finally, the research clusters are studied to understand the literature's main themes and develop the future research agenda using content analysis.

The results indicate that publications on NFF are rising quickly in the last decade, especially after 2010. The previous NFF research primarily focuses on system design, fault diagnostics, reliability engineering, data management and human factors, but the criticality of economic and risk analysis has not been significantly represented.

The study resulted in developing an inclusive framework and identifying six research clusters that will help in granular understanding, benefit the researchers, practitioners and policy formulators in NFF.

This study examines the NFF's current research direction and calls for further research in integrating NFF economics on its stakeholders like manufacturers, supply chain, customers and risk analysis during the product life cycle.

The purpose of this paper is to report the findings of a study that used measurements of shaft relative rotational position, made using inexpensive Hall Effect sensors and magnets mounted at the ends of a gearbox input and output shafts, to determine gear “transmission variance.” The transmission variance signals, as a function of gear/shaft rotational position, were then used to detect and diagnose gear faults.

Two sets of spur gears (one plastic and one steel) were used to experimentally determine the relative shaft rotational position between the input and the output gearbox shafts. Fault-free and faulted (damaged tooth faces and cracked tooth bases) gears were used to collect representative dynamic signals. Signal processing was used to extract transmission variance values as a function of shaft rotational position and then used to detect and diagnose gear faults.

The results show that variations in the relative rotational position of the output shaft relative to that of the input shaft (the transmission variance) can be used to reveal gear mesh characteristics, including faults, such as cracked or missing gear teeth and flattened gear tooth faces, in both plastic gears and steel gears under appropriate (realistic) loads and speeds.

The operational mode of the non-contact rotational position sensors and the dynamic accuracy limitations are explained along with the basic signal processing required to extract transmission variance values.

The results show that shaft rotational position measurements can be made accurately and precisely using relatively inexpensive sensors and can subsequently reveal gear faults.

The inexpensive and yet trustworthy fault detection methodology developed in this work should help to improve the efficiency of maintenance actions on gearboxes and, therefore, improve the overall industrial efficiency of society.

The method described has distinct advantages over traditional analysis methods based on gearbox vibration and/or oil analysis.

In manufacturing systems intelligent techniques are being used to integrate and interpret data from multiple sensors to predict tool wear and tool life. Less attention is devoted to developments of integrated condition monitoring systems, which enable the user to evaluate a multi‐variant system based on the data collected from, e.g. maintenance, quality, production, etc. In this paper we discussed different approaches of how to keep availability, quality and productivity at high levels. Also, we proposed a new approach for an expert system concept, which is characterised by using a total quality maintenance (TQMain) concept; having a common database, and a continuously improved knowledge base with an intelligent inference engine. It can enhance data reliability, decision making certainty, remove the redundancy in monitoring systems, and allow the user to detect and eliminate reasons behind variations through effective diagnosis and prognosis. This will enhance the performance‐efficiency, availability and quality rate, i.e. overall equipment effectiveness of the manufacturing systems.

The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.

The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.

The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.

The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

The purpose of this paper is to present a new model to study inter‐turn short circuit faults in a permanent magnet synchronous machine.

The machine is modeled by using classical two‐axis theory, and the equations are modified to take into account the stator inter‐turn faults. A state space form of the system is presented for dynamic simulations.

The machine model is global and can work in both normal and fault conditions due to a fictitious resistance in the winding circuit. Various simulation results have been presented indicating the fault instant and its corresponding effect. Validation is carried out by transient time finite element simulations.

The model can serve as a step towards development of fault detection and diagnosis algorithms.

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.

The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.

The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.

The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.