Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study

A large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future

The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

A dependability framework for WSN-based aquatic monitoring systems

Wireless Sensor Networks (WSN) are being progressively used in several application areas, particularly to collect data and monitor physical processes. Moreover, sensor nodes used in environmental monitoring applications, such as the aquatic sensor networks, are often subject to harsh environmental conditions while monitoring complex phenomena. Non-functional requirements, like reliability, security or availability, are increasingly important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provides a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data is reliable or, more generically, that it has the necessary quality. The problem of ensuring the desired quality of data for dependable monitoring using WSNs is studied herein. With a dependability-oriented perspective, it is reviewed the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, it is given particular attention to understanding which faults can affect sensors, how they can affect the quality of the information, and how this quality can be improved and quantified. Open research issues for the specific case of aquatic monitoring applications are also discussed. One of the challenges in achieving a dependable system behavior is to overcome the external disturbances affecting sensor measurements and detect the failure patterns in sensor data. This is a particular problem in environmental monitoring, due to the difficulty in distinguishing a faulty behavior from the representation of a natural phenomenon. Existing solutions for failure detection assume that physical processes can be accurately modeled, or that there are large deviations that may be detected using coarse techniques, or more commonly that it is a high-density sensor network with value redundant sensors. This thesis aims at defining a new methodology for dependable data quality in environmental monitoring systems, aiming to detect faulty measurements and increase the sensors data quality. The framework of the methodology is overviewed through a generically applicable design, which can be employed to any environment sensor network dataset. The methodology is evaluated in various datasets of different WSNs, where it is used machine learning to model each sensor behavior, exploiting the existence of correlated data provided by neighbor sensors. It is intended to explore the data fusion strategies in order to effectively detect potential failures for each sensor and, simultaneously, distinguish truly abnormal measurements from deviations due to natural phenomena. This is accomplished with the successful application of the methodology to detect and correct outliers, offset and drifting failures in real monitoring networks datasets. In the future, the methodology can be applied to optimize the data quality control processes of new and already operating monitoring networks, and assist in the networks maintenance operations.As redes de sensores sem fios (RSSF) têm vindo cada vez mais a serem utilizadas em diversas áreas de aplicação, em especial para monitorizar e capturar informação de processos físicos em meios naturais. Neste contexto, os sensores que estão em contacto direto com o respectivo meio ambiente, como por exemplo os sensores em meios aquáticos, estão sujeitos a condições adversas e complexas durante o seu funcionamento. Esta complexidade conduz à necessidade de considerarmos, durante o desenvolvimento destas redes, os requisitos não funcionais da confiabilidade, da segurança ou da disponibilidade elevada. Para percebermos como satisfazer estes requisitos da monitorização com base em RSSF para aplicações ambientais, já existe uma boa base de conhecimento sobre técnicas de confiabilidade em sistemas distribuídos. Devido ao foco na obtenção de dados deste tipo de aplicações de RSSF, é particularmente importante garantir que os dados obtidos na monitorização sejam confiáveis ou, de uma forma mais geral, que tenham a qualidade necessária para o objetivo pretendido. Esta tese estuda o problema de garantir a qualidade de dados necessária para uma monitorização confiável usando RSSF. Com o foco na confiabilidade, revemos os possíveis impedimentos à obtenção de dados confiáveis e as soluções existentes capazes de corrigir ou mitigar esses impedimentos. Apesar de existir uma grande variedade de componentes que formam ou podem formar um sistema de monitorização com base em RSSF, prestamos particular atenção à compreensão das possíveis faltas que podem afetar os sensores, a como estas faltas afetam a qualidade dos dados recolhidos pelos sensores e a como podemos melhorar os dados e quantificar a sua qualidade. Tendo em conta o caso específico dos sistemas de monitorização em meios aquáticos, discutimos ainda as várias linhas de investigação em aberto neste tópico. Um dos desafios para se atingir um sistema de monitorização confiável é a deteção da influência de fatores externos relacionados com o ambiente monitorizado, que afetam as medições obtidas pelos sensores, bem como a deteção de comportamentos de falha nas medições. Este desafio é um problema particular na monitorização em ambientes naturais adversos devido à dificuldade da distinção entre os comportamentos associados às falhas nos sensores e os comportamentos dos sensores afetados pela à influência de um evento natural. As soluções existentes para este problema, relacionadas com deteção de faltas, assumem que os processos físicos a monitorizar podem ser modelados de forma eficaz, ou que os comportamentos de falha são caraterizados por desvios elevados do comportamento expectável de forma a serem facilmente detetáveis. Mais frequentemente, as soluções assumem que as redes de sensores contêm um número suficientemente elevado de sensores na área monitorizada e, consequentemente, que existem sensores redundantes relativamente à medição. Esta tese tem como objetivo a definição de uma nova metodologia para a obtenção de qualidade de dados confiável em sistemas de monitorização ambientais, com o intuito de detetar a presença de faltas nas medições e aumentar a qualidade dos dados dos sensores. Esta metodologia tem uma estrutura genérica de forma a ser aplicada a uma qualquer rede de sensores ambiental ou ao respectivo conjunto de dados obtido pelos sensores desta. A metodologia é avaliada através de vários conjuntos de dados de diferentes RSSF, em que aplicámos técnicas de aprendizagem automática para modelar o comportamento de cada sensor, com base na exploração das correlações existentes entre os dados obtidos pelos sensores da rede. O objetivo é a aplicação de estratégias de fusão de dados para a deteção de potenciais falhas em cada sensor e, simultaneamente, a distinção de medições verdadeiramente defeituosas de desvios derivados de eventos naturais. Este objectivo é cumprido através da aplicação bem sucedida da metodologia para detetar e corrigir outliers, offsets e drifts em conjuntos de dados reais obtidos por redes de sensores. No futuro, a metodologia pode ser aplicada para otimizar os processos de controlo da qualidade de dados quer de novos sistemas de monitorização, quer de redes de sensores já em funcionamento, bem como para auxiliar operações de manutenção das redes.Laboratório Nacional de Engenharia Civi

An investigation into the effects of thermal errors of a machine tool on the dimensional accuracy of parts

The reduction of machining errors has become increasingly important in modern manufacturing in order to obtain the required quality of parts. Geometric error makes up the basic part of the inaccuracy of the machine tool at the cold stage; however, as the machine running time increases, thermally-induced errors start to play a major role in machined workpiece accuracy. Dimensional accuracy of machined parts could be affected by several factors, such as the machine tool’s condition, the workpiece material, machining procedures and the operator’s skill. Of these, the machine condition plays an important role in determining the machine’s performance and its effects on the final dimensions of machined parts. The machine’s condition can be evaluated by its errors which include the machine’s built-in geometric and kinematic error, thermal error, cutting force-induced error and other errors.This research represents a detailed study of the effects of thermal errors of a machine tool on the dimensional accuracy of the parts produced on it. A new model has been developed for the prediction of thermally-induced errors of a three-axis machine tool. By applying the proposed model to real machining examples, the dimensional accuracy of machined parts was improved. The research work presented in this thesis has the following four unique characteristics:• Investigated the thermal effects on the dimensional accuracy of machined parts by machining several components at different thermal conditions of a machine tool to establish a direct relationship between the dimensional accuracy of machined parts and the machine tool’s thermal status.• Developed a new model for calculating thermally-induced volumetric error where the three axial positioning errors were modelled as functions of ball screw nut temperature and travel distance. The influences of the other 18 error components were ignored due to their insignificant influence.• Employed a Laser Doppler Displacement Meter (LDDM) with three thermocouples, instead of the expensive laser interferometer and the large number of thermocouples required by the traditional model, to assess the thermally-induced volumetric errors of a three-axis CNC machining centre. The thermally-induced volumetric error predictions were in good agreement with the measured results.• Applied the newly developed thermally-induced volumetric error compensation model for drilling operations to improve the positioning accuracy of drilled holes. The results show that positioning accuracy of the drilled holes was improved significantly after compensation. The absolute reduction of the positioning errors of drilled holes was an average 30.44 μm at the thermal stable stage, while the average relative reduction ratio of these errors was 77%.Therefore, the proposed thermally-induced volumetric error compensation model can bean effective tool for enhancing the machining accuracy of existing machine tools used in the industry

A sensorless state estimation for a safety-oriented cyber-physical system in urban driving : deep learning approach

In today's modern electric vehicles, enhancing the safety-critical cyber-physical system CPS 's performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach. A deep neural network DNN is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.Published versio

Human skill capturing and modelling using wearable devices

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution

Applications of MEMS Gyroscope for Human Gait Analysis

After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis