Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.

<div> <p>Sound and vibration analysis is a prominent tool used for scientific investigations in various fields such as structural model identification or dynamic behavior studies. In manufacturing fields, the vibration signals collected through commercial sensors are utilized to monitor machine health, for sustainable and cost-effective manufacturing.</p> <p> Recently, the development of commercial sensors and computing environments have encouraged researchers to combine gathered data and Machine Learning (ML) techniques, which have been proven to be efficient for categorical classification problems. These discriminative algorithms have been successfully implemented in monitoring problems in factories, by simulating faulty situations. However, it is difficult to identify all the sources of anomalies in a real environment. </p> <p>In this paper, a Neural Network (NN) application on a KUKA KR6 robot arm is introduced, as a solution for the limitations described above. Specifically, the autoencoder architecture was implemented for anomaly detection, which does not require the predefinition of faulty signals in the training process. In addition, stethoscopes were utilized as alternative sensing tools as they are easy to handle, and they provide a cost-effective monitoring solution. To simulate the normal and abnormal conditions, different load levels were assigned at the end of the robot arm according to the load capacity. Sound signals were recorded from joints of the robot arm, then meaningful features were extracted from spectrograms of the sound signals. The features were utilized to train and test autoencoders. During the autoencoder process, reconstruction errors (REs) between the autoencoder’s input and output were computed. Since autoencoders were trained only with features corresponding to normal conditions, RE values corresponding to abnormal features tend to be higher than those of normal features. In each autoencoder, distributions of the RE values were compared to set a threshold, which distinguishes abnormal states from the normal states. As a result, it is suggested that the threshold of RE values can be utilized to determine the condition of the robot arm.</p> </div> <br>

In 2016 the average industry downtime cost was estimated to $260.000 every hour, and with Swedish industries being an important part of the national economy it would be desirable to reduce the amount of unplanned downtime to a minimum. There are currently many different solutions for system supervision for monitoring system health but none which analyse data with machine learning in an industrial gateway.   The aim for this thesis is to test, compare and evaluate three different algorithms to find a classifier suitable for a gateway environment. The evaluated algorithms were Random Forest, K-Nearest Neighbour and Linear Discriminant Analysis. Load imbalance detection was used as a case study for evaluating these algorithms. The gateway received data from a Modbus ATV32 frequency converter, which measured specific features from an induction motor. The imbalance was created with loads that were attached on a fly-wheel at different angles to simulate different imbalances. The classifiers were compared on their accuracy, memory usage, CPU usage and execution time. The result was evaluated with tables, confusion matrices and AUC- ROC curves.  Although all algorithms performed well LDA was best based on the criteria set.

Identificar e localizar faltas em alimentadores de distribuição representa um passo importante para a melhoria da qualidade de energia, pois proporciona impactos diretos sobre o tempo de inspeção. Na verdade, a duração da inspeção implica consideravelmente no intervalo em que os consumidores estão sem energia elétrica, quando ocorre uma interrupção não programada. O objetivo deste trabalho é fornecer um sistema de detecção automática de curtos-circuitos, permitindo aos profissionais das companhias de distribuição acompanhar e monitorar de maneira on-line a ocorrência de possíveis faltas e transitórios eletromagnéticos observados na rede primária de distribuição. A abordagem de detecção utiliza um sistema híbrido que combina ferramentas inteligentes e convencionais para identificar e localizar faltas em redes primárias. Os resultados que foram compilados demonstram grande potencialidade de aplicação da proposta em sistemas de distribuição. / Efficient faults identification and location in power distribution lines constitute an important step for power quality improvement, since they provide direct impacts on the inspection time. In fact, the duration of inspection implies directly in the time interval where consumers are without power, considering here the occurrence of a non-programmed interruption. The objective of this work is to provide an automated fault detection system, allowing to the power companies engineers to online track and monitor the possible occurrence of faults and electromagnetic transients observed in the primary network for the distribution circuits. The detection approach uses a hybrid system, which combines a set of intelligent and conventional tools to identify and locate faults in the primary networks. Validation results show great application potential in distribution systems.

A demanda crescente na complexidade, eficiência e confiabilidade nos sistemas industriais modernos têm estimulado os estudos da teoria de controle aplicada no desenvolvimento de sistemas de Monitoração e Detecção de Falhas. Neste trabalho foi desenvolvida uma metodologia inédita de Monitoração e Detecção de Falhas através do algoritmo GMDH e Redes Neurais Artificiais (RNA) que foi aplicada ao reator de pesquisas do IPEN, IEA-R1. O desenvolvimento deste trabalho foi dividido em duas etapas: sendo a primeira etapa dedicada ao pré-processamento das informações, realizada através do algoritmo GMDH; e a segunda o processamento das informações através de RNA. O algoritmo GMDH foi utilizado de duas maneiras diferentes: primeiramente, o algoritmo GMDH foi utilizado para gerar uma melhor estimativa da base de dados, tendo como resultado uma matriz denominada matriz_z, que foi utilizada no treinamento das RNA. Logo após, o GMDH foi utilizado no estudo das variáveis mais relevantes, sendo estas variáveis utilizadas no processamento das informações. Para realizar as simulações computacionais, foram propostos cinco modelos: Modelo 1 (Modelo Teórico) e Modelos 2, 3, 4 e 5 (Dados de operação do reator). Após a realização de um estudo exaustivo dedicado a Monitoração, iniciou-se a etapa de Detecção de Falhas em sensores, onde foram simuladas falhas na base de dados dos sensores. Para tanto as leituras dos sensores tiveram um acréscimo dos seguintes valores: 5%, 10%, 15% e 20%. Os resultados obtidos utilizando o algoritmo GMDH na escolha das melhores variáveis de entrada para as RNA foram melhores do que aqueles obtidos utilizando apenas RNA, o que viabiliza o uso da nova metodologia de Monitoração e Detecção de Falhas em sensores apresentada. / The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix_z, which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors.

<p>The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance.</p><p>One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is model-based fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is fault-free, and non-zero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation.</p><p>The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by Differential-Algebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observer-based residual generation for linear DAE-models. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in state-space form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. null-space calculations and equivalence transformations.</p><p>In the second method, non-linear DAE-models are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system.</p>

<p>Diagnosis means detection and isolation of faults. A model based diagnosis system is built on a mathematical model of the system. The difficulty when constructing the diagnosis system depends om how the model is formulated. In this report, a method is described that rewrites the model on such a form that the construction of the diagnosis algoritm is easy. The model is transformed by two state space transformations and the result will be a system on state space form where one part of the system becomes easy to supervise. </p><p>The main part of the report describes the procedure to create these transformations, which can be done in seven steps, based on differential geometric methods. </p><p>The aim of this masters thesis was to create an implementation in Mathematica (a computer tool for symbolic formula manipulation) of the creation of the two transformations and the system transformation. The created functions are described and examples of these are given. </p><p>A further aim was to evaluate if Mathematica could be a good support to rewrit a model. This was done by studying examples, and on the basis of the examples, identify difficult and easy steps. </p><p>The program has shown to be a good aid. Two of the seven steps have been identified as difficult and proposals for improvements have been given.</p>

<p>Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. </p><p>A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. </p><p>The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.</p>

<p>This master’s thesis work was performed at Volvo Powertrain in Köping, Sweden, which manufactures gearboxes and integrated transmission systems for heavy vehicles. The thesis is a continuation of a previous master’s thesis performed at the Köping factory in 2009. After manufacturing and assembly, each gearbox is manually validated to ensure the gearbox quality and functionality. When validating the gearbox gears, the operator shifts the gearbox in a predefined manner and listens for irregularities. If an error sound is heard the operator must then locate the source of error. With numerous of cog wheels rotating at the same time this task requires extensive knowledge and experience of the operator. The main objective is to develop an automatic diagnostic system for detection of cog errors and assist the operator in the process of locating the faulty component.</p><p>The work consists of two parts. In the first part the automatic diagnostic system is developed and a database of gearbox recordings is stored. The amounts of logged non-faulty gearboxes are significantly much larger (50) than the logged faulty gearboxes (1). Therefore, when determining thresholds needed for the diagnosis, the data obtained from the non-faulty gearboxes are used. Two statistical methods are presented to extract the thresholds. The first method uses an extremevalue distribution and the other method a Gaussian distribution. When validated, both methods did successfully detect on cog faults. In the second part an investigation is made of how shaft imbalance can be detected and implemented in the developed system.</p><p>Volvo Powertrain continually follows-up all faults found at the validation station to ensure the quality of their work and eliminate the sources of error. During system testing one logged gearbox was found faulty. The automatic diagnostic system did successfully detect and locate the faulty component which later also was confirmed when the gearbox was dismounted. With only one detected error it is difficult to conclude the system performance and further testing is required. However, during the testing no false detections were made.</p>

<p>This thesis presents techniques for improving building HVAC system performance in existing buildings generated using simulation-based tools and real data. Therefore, one of the aims has been to research the needs and possibilities to assess and improve building HVAC system performance. In addition, this thesis aims at an advanced utilization of building energy management system (BEMS) and the provision of useful information to building operators using simulation tools.</p><p>Buildings are becoming more complex systems with many elements, while BEMS provide many data about the building systems. There are, however, many faults and issues in building performance, but there are legislative and cost-benefit forces induced by energy savings. Therefore, both BEMS and the computer-based tools have to be utilized more efficiently to improve building performance.</p><p>The thesis consists of four main parts that can be read separately. The first part explains the term commissioning and the commissioning tool work principal based on literature reviews. The second part presents practical experiences and issues introduced through the work on this study. The third part deals with the computer-based tools application in design and operation. This part is divided into two chapters. The first deals with improvement in the design, and the second deals with the improvement in the control strategies. The last part of the thesis gives several rules for fault diagnosis developed using simulation tools. In addition, this part aims at the practical explanation of the faults in the building HVAC systems.</p><p>The practical background for the thesis was obtained though two surveys. The first survey was carried out with the aim to find the commissioning targets in Norwegian building facilities. In that way, an overview of the most typical buildings, HVAC equipment, and their related problems was obtained. An on-site survey was carried out on an example building, which was beneficial for introducing the building maintenance structure and the real hydronic heating system faults.</p><p>Coupled simulation and optimization programs (EnergyPlus and GenOpt) were utilized for improving the building performances. These tools were used for improving the design and the control strategies in the HVAC systems. Buildings with a hydronic heating system were analyzed for the purpose of improving the design. Since there are issues in using the optimization tool, GenOpt, a few procedures for different practical problems have been suggested. The optimization results show that the choice of the optimization functions influences significantly the design parameters for the hydronic heating system.</p><p>Since building construction and equipment characteristics are changing over time, there is a need to find new control strategies which can meet the actual building demand. This problem has been also elaborated on by using EnergyPlus and GenOpt. The control strategies in two different HVAC systems were analyzed, including the hydronic heating system and the ventilation system with the recovery wheel. The developed approach for the strategy optimization includes: involving the optimization variables and the objective function and developing information flow for handling the optimization process.</p><p>The real data obtained from BEMS and the additional measurements have been utilized to explain faults in the hydronic heating system. To couple real data and the simple heat balance model, the procedure for the model calibration by use of an optimization algorithm has been developed. Using this model, three operating faults in the hydronic heating system have been elaborated.</p><p>Using the simulation tools EnergyPlus and TRNSYS, several fault detection and diagnosis (FDD) rules have been generated. The FDD rules were established in three steps: testing different faults, calculating the performance indices (PI), and classifying the observed PIs. These rules have been established for the air cooling system and the hydronic heating system. The rules can diagnose the control and the component faults. Finally, analyzing the causes and the effects of the tested faults, useful information for the building maintenance has been descriptively explained.</p><p>The most important conclusions are related to a practical connection of the real data and simulation-based tools. For a complete understanding of system faults, it is necessary to provide real-life information. Even though BEMS provides many building data, it was proven that BEMS is not completely utilized. Therefore, the control strategies can always be improved and tuned to the actual building demands using the simulation and optimization tools. It was proven that many different FDD rules for HVAC systems can be generated using the simulation tools. Therefore, these FDD rules can be used as manual instructions for the building operators or as a framework for the automated FDD algorithms.</p> / <p>Denne avhandlingen presenterer noen fremgangsmåter for forbedring av ytelser for VVS-tekniske anlegg i eksisterende bygninger basert på bruk av simuleringsverktøy og virkelige måledata. Ett av målene har vært å undersøke behov og muligheter for vurdering og forbedring av ytelser for VVS-anlegg i bygninger. I tillegg har denne avhandlingen hatt som mål å fremme bruk av SD-anlegg samt å fremskaffe nyttig informasjon til driftspersonalet.</p><p>Bygninger blir stadig mer kompliserte systemer som inneholder flere og flere komponenter mens SD-anlegg håndterer en stadig større mengde data fra bygningsinstallasjoner. På den ene siden registreres det ofte feil og problemer med hensyn til ytelsene til de VVS-tekniske installasjonene. På den andre siden innføres det stadig strengere lovmessige pålegg og kost-nyttekrav motivert i energieffektiviseringen. SD-anlegg og databaserte verktøy bør derfor brukes mer effektivt for forbedring av ytelsene.</p><p>Avhandlingen består av fire hoveddeler hvor hver del kan leses separat. Den første delen, som er basert på literatturstudie, forklarer funksjonskontroll som begrep og prinsipper for oppbygging av verktøy for funksjonskontroll. Den andre delen presenterer praktisk erfaring og problemstillinger utviklet og behandlet i løpet av arbeidet med avhandlingen. Den tredje delen handler om anvendelse av databaserte verktøy for forbedring av ytelsen for VVS-tekniske installasjoner. Den tredje delen er delt opp i to kapitler, hvorav et handler om forbedring av systemløsninger og et om forbedring av styringsstrategier. Den siste delen presenterer flere regler for feilsøking og diagnostisering utviklet gjennom bruk av simuleringsverktøy. I tillegg gir denne delen en praktisk forklaring av feilene i de VVS-anleggene som er behandlet i undersøkelsen.</p><p>Det praktiske grunnlaget for avhandlingen er etablert gjennom to undersøkelser. Den første var en spørreundersøkelse som hadde til hensikt å kartlegge målsetninger for funksjonskontroll i norske bygninger. Gjennom dette ble det etablert en oversikt over de mest typiske bygninger med tilhørende VVS-anlegg og de mest forekommende problemene. En dypere undersøkelse ble utført på ett casebygg. Denne undersøkelsen viste seg å være nyttig både for kartlegging av betydningen av organisering av driften av bygningen og for avdekking av de virkelige feilene i det vannbårne oppvarmingssystemet.</p><p>En kobling mellom et simulerings- og et optimaliseringsprogram (EnergyPlus og GenOpt) har vært benyttet for forbedring av ytelsene for de VVS-tekniske installasjonene. Disse verktøyene har vært brukt for forbedring av både systemløsningene og styringsstrategiene for VVS-anlegg. Bygninger med vannbåren oppvarmingssystem har vært analysert for å forbedre systemløsningen. På grunn av begrensninger i bruken av optimaliseringsverktøyet GenOpt, har det blitt utviklet egne prosedyrer for håndtering av visse typer problemstillinger hvor denne begrensningen opptrer. Resultatene for optimaliseringen viser at valg av objektfunksjoner påvirker betydelig parametrene i det vannbårne oppvarmingssystemet.</p><p>Endringer i egenskapene til både bygningskonstruksjoner og utstyr som skjer på grunn av aldring over tiden, gjør det nødvendig med tilpassning av styringsstrategier slik at det virkelige behovet kan bli dekket. Denne problemstillingen har vært analysert ved bruk av EnergyPlus og GenOpt. Styringsstrategiene for to forskjellige VVS-anlegg, et vannbåret oppvarmingssystem og et ventilasjonsanlegg med varmegjenvinner har blitt behandlet. Den utviklete prosedyren for optimalisering av styringsstrategien består av følgende steg: innføring av optimaliseringsvariabler og objektfunksjon, samt utvikling av informasjonsflyt for behandling av optimaliseringsprosessen.</p><p>De virkelige data, både fra SD-anlegg og tilleggsmålinger, har vært benyttet for praktisk forklaring av feilene i oppvarmingssystemet. En prosedyre for modellkalibrering basert på bruk av en optimaliseringsalgoritme som kobler sammen de virkelige data og en enkel varmebalansemodell har blitt foreslått. Tre konkrete driftsfeil i oppvarmingssystemet har blitt belyst gjennom bruk av denne varmebalansemodellen.</p><p>Flere regler for feilsøking og diagnostisering har blitt utviklet ved hjelp av simuleringsverktøyene EnergyPlus and TRNSYS. Denne utviklingen har bestått av tre ulike steg: testing av bestemte feil, beregning av ytelsesindikatorer og til slutt klassifisering av de observerte ytelsesindikatorer. Reglene har blitt utviklet for et system av aggregater for luftkjøling og for et vannbåret oppvarmingssystem. Reglene kan diagnostisere både styringsfeil og komponentfeil. Til slutt presenteres informasjon som er nyttig for drift av VVS-tekniske installasjoner i bygninger basert på en analyse av årsakene for og virkningene av de feil som er behandlet.</p><p>De viktigste konklusjonene er knyttet til praktisk kombinasjon av virkelige måleverdier og simuleringsverktøy. Informasjon fra det virkelig liv er helt nødvendig for å få en god forståelse av feil som oppstår i anlegg. Det er også vist at potensialet som ligger i alle de data som er tilgjengelige gjennom SD-anlegg, ikke er fullt utnyttet. Gjennom bruk av simuleringsverktøy kan styringsstrategiene alltid bli bedre tilpasset og innjustert til de virkelige behov i bygningen. Simuleringsverktøy kan også brukes for utvikling av prosedyrer for feilsøking og diagnostisering i VVS-tekniske anlegg. Disse prosedyrene kan brukes enten som en veileder for manuell feilsøking og detektering eller som grunnlag for utvikling av automatiserte algoritmer.</p> / Paper II, VI and VII are reprinted with kind permission from Elsevier, sciencedirect.com