The project covers the substitution of a stamping shut height measurement at the chainsaw chain factory Husqvarna Edge, with a new sensor-based processmonitoring system, set to increase productivity and decrease running costs. The workflow covers the span from external and needs analysis to development and testing of both hardware and software, in conjunction with communication with external suppliers to find the most usable and profitable solution going forward. Regarding hardware was inductive / eddy-current distance measurement sensors the most suitable for the stamping press environment, providing high sample rate and repeatability to a micrometre level, while subjected to oil andvibration. Husqvarna’s state of the art stamping setup is not yet fully supported by process monitoring suppliers, which resulted in the creation of the new program called Live Height, which was successfully used for sensor evaluation and serves as an example for suppliers to expand upon. This work provides several options in different price ranges for Husqvarna’s management to make decisions upon.

This thesis aims to investigate the possibility of using a mathematical model to detect several common faults in a hydraulic rock drill. To this end, a parameterized state space model of the hydraulic drill, which simulate its behavior, is created. The model parameters are divided into two categories where different estimation methods are used to determine their values. The first category consists mainly of the parameters that are assumed to be invariant and independent of the various operating conditions. Experimental data are used to estimate these parameters. The other category is the variables that change depending on the machine’s current condition and operating settings. These include the response from the rock and internal leakages in the hydraulic drill. These parameters are estimated by integrating the impact piston position measurements in the simulation algorithm. The model is simulated for different fault modes, and the resulting estimated parameters are studied. It is shown that the resulting distributions for some of the estimated parameters differ between the fault modes, which makes fault detection possible. Furthermore, a condition monitoring system based on the estimated parameters provided by the model is designed and evaluated. It is shown that the performance and the robustness of the monitoring system depend on the machine’s operating settings and condition, where the system performs best for an operating pressure of 220 bar and the internal cylinder leakages.

The electrical power grid is one of the most important infrastructures in the modernsociety. It supplies industrial and private customers with electricity and supportsother critical infrastructures such as the water supply. Thus, it is significant that the power grid is a reliable system. However, the power system experiences a hugetransition from classical production methods such as coal and nuclear power plantsto distributed renewable energy forms such as wind energy and photovoltaic. This change to a more distributed system challenges the existing power grid as well as the traditional business models of electric utilities. Thus, cost minimization to increase profitability and improvement of the power grid to increase customer satisfactionare in the focus. One approach to increase the reliability of the grid and decrease maintenance costs is a condition-based maintenance approach which requirescondition monitoring techniques. This thesis introduces into failure rate modelling for individual power system components and develops a method to calculate individual failure rates based onthe average failure rate, failure statistics, and condition monitoring data. This approach includes the analysis of failure statistics to identify failure causes and failure locations which are population characteristics but can be utilized to describe the heterogeneity within the population. Thus, the thesis first introduces into the topic of failure analysis and heterogeneity in populations. Different factors are identified and categorized which describe the condition development of a component overtime. Then, the literature within failure rate estimation is reviewed to present the factors which are used within failure rate modelling and to outline the existingmethods which consider the individual. However, limitations are discussed which emphasize the demand for a new approach. Consequently, this thesis introduce intoa new approach for estimating the failure rate for individual components. / <p>QC 20160526</p> / Energiforsk AB risk analysis program

With increasing digitization, models are more important than ever. Especially their use as soft sensors during operation offers opportunities in cost saving, easy data acquisition and therefore additional functionality of systems. In soft sensor networks there is redundant data acquisition and consequently the occurrence of inconsistent values from different soft sensors is encouraged. The resolution of these data-induced conflicts allows for the detection of changing components characteristics. Hence soft sensor networks can be used to detect wear in system components. In this paper this approach is validated on a test rig. It is found, that the soft sensor network is capable to determine wear and its extent in eccentric screw pumps and valves via data induced conflicts with relatively simple models.

The on-line condition monitoring of turbomachinery blades is of utmost importance to ensure the long term health and availability of such machines and as such has been an area of study since the late 1960s. As a result a number of on-line blade vibration measurement techniques are available, each with its own associated advantages and shortcomings. In general, on-blade sensor measurement techniques suffer from sensor lifespan, whereas non-contact techniques usually have measurement bandwidth limitations. One non-contact measurement technique that yields improvements in the area of measurement bandwidth is laser Doppler vibrometry. This thesis presents results and findings from utilizing laser Doppler vibrometry in an Eulerian fashion (i.e. a fixed reference frame) to measure on-line blade vibrations in axial-flow turbomachinery. With this measurement approach, the laser beam is focussed at a fixed point in space and measurements are available for the periods during which each blade sweeps through the beam. The characteristics of the measurement technique are studied analytically with an Euler-Bernoulli cantilever beam and experimental verification is performed. An approach for the numerical simulation of the measurement technique is then presented. Associated with the presented measurement technique are the short periods during which each blade is exposed to the laser beam. This characteristic yields traditional frequency domain signal processing techniques unsuitable for providing useful blade health indicators. To obtain frequency domain information from such short signals, it is necessary to employ non-standard signal processing techniques such as non-harmonic Fourier analysis. Results from experimental testing on a single-blade test rotor at a single rotor speed are presented in the form of phase angle trends obtained with non-harmonic Fourier analysis. Considering the maximum of absolute unwrapped phase angle trends around various reference frequencies, good indicators of blade health deterioration were obtained. These indicators were verified numerically. To extend the application of this condition monitoring approach, measurements were repeated on a five-blade test rotor at four different rotor speeds. Various damage cases were considered as well as different ELDV measurement positions. Using statistical parameters of the abovementioned indicators as well as time domain parameters, it is shown that with this condition monitoring approach, blade damage can successfully be identified and quantified with the aid of artificial neural networks. / Thesis (PhD)--University of Pretoria, 2010. / Mechanical and Aeronautical Engineering / unrestricted

Optimizing the operation and maintenance of wind turbines is crucial as the wind energy sector continues to expand. Predicting the mass imbalance of wind turbines, which can seriously damage the rotor blades, gearbox, and other components, is one of the key issues in this field. In this work, we propose a machine learning-based method for predicting the mass imbalance of wind turbines utilizing information from multiple sensors and monitoring systems. We collected data and trained the model from Adwen AD8 wind turbine model and evaluated on the real wind turbine SCADA data which is located at Fraunhofer IWES, Bremerhaven. The data included various parameters such as wind speed, blade root bending moments and rotor speed. We used this data to train and test machine learning classification models based on different algorithms, including extra-tree classifiers, support vector machines, and random forest. Our results showed that the machine learning models were able to predict the mass imbalance percentage of wind turbines with high accuracy. Particularly, the extra tree classifiers with blade root bending moments outperformed other research for multiclassification problem with an F1 score of 0.91 and an accuracy of 90%. Additionally, we examined the significance of various features in predicting the mass imbalance and observed that the rotor speed and blade root bending moments were the most crucial variables. Our research has significant effects for the wind energy sector since it offers a reliable and efficient way for predicting wind turbine mass imbalance. Wind farm operators can save maintenance costs, minimize downtime of wind turbines, and increase the lifespan of turbine components by identifying and eliminating mass imbalances. Also, further investigation will allow us to apply our method to different kinds of wind turbines, and it is simple to incorporate into current monitoring systems as it supports prediction without installing additional sensors. In conclusion, our study demonstrates the potential of machine learning for predicting the percentage of mass imbalance of wind turbines. We believe that our approach can significantly benefit the wind energy industry and contribute to the development of sustainable energy sources.

Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches. In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model. The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.

The objective of this thesis work was to develop a method to detect early damage on small planetary gear sets that are installed in Atlas Copco nutrunners. The project has gone through several stages of product development, from idea to working product and signal analysis. Currently, Atlas Copco have a test rig for testing these planetary gears, this rig has been proven to be insufficient at detecting faults during an ongoing test. A new tailored test rig was therefore designed and manufactured. Low noise and low amount of vibration was of interest when designing the rig. Four concepts was thought of and evaluated through simulations using Matlab and Simulink. Most of the components of the rig were manufactured in the workshop at Atlas Copco in Nacka. Methods fo rmeasuring torsional, transverse and acoustic vibration was implemented and analyzed. There are many different parameters considering fault of fixed shaft gears. However, these are not easily applicable on a planetary gear because of the nature of its design. Therefore, special techniques are required. Two “new” parameters were tested (NSDS,FRMS [Lei. et al.]) with positive results. Pitting of individual gear members could befound.

Condition monitoring techniques can be employed to enhance reliability of electric machinery. The stator winding fault is one of the dominant causes for the failure of induction machines. In this work, the condition monitoring of an inverter-fed induction machine using high-frequency signal injection based technique is investigated. Initially, an analytical model of the induction machine with a stator inter-turn fault is developed. Subsequently, the behaviour of the induction machine in the presence of stator inter-turn fault is analyzed using the symmetrical component theory. Because of their use for fault diagnosis purposes, the analytical expressions for the fundamental and high-frequency symmetrical component currents are derived. The high-frequency signal injection is performed by adding a balanced three-phase high-frequency low-magnitude voltage to the fundamental excitation voltage. The resulting high-frequency negative-sequence current component can be used as reliable fault indicator to detect stator inter-turn faults. The effectiveness of the high-frequency negative-sequence current as a fault indicator is compared with the fundamental negative-sequence current, which is one of the traditionally used fault indicators for detecting these faults. The high-frequency signal injection technique proposed in this work is tested experimentally on a prototype machine in a laboratory set-up. The use of the proposed fault indicator is found to be advantageous when compared to the use of the traditional fault indicator for variable-frequency drives. In particular, it is shown that the proposed fault indicator is less dependent from the drive operating conditions than the traditional fault indicator. / Tillståndsövervakning är en teknik som kan användas för att förbättra tillförlitligheten hos elektriska maskiner. För asynkronmaskiner är fel i statorlindningen en av de dominerande orsakerna som leder till problem. I detta arbete undersöks tillståndsövervakning av en omriktarmatad asynkronmotor med hjälp av en högfrekvent signalinjektionsbaserad teknik. Inledningsvis utvecklas en analytisk modell av en asynkronmaskin med korsslutningsfel mellan varven i statorn. Därefter analyseras beteendet hos maskinen med hjälp av teorin för symmetriska komponenter. Analytiska uttryck för både grund- och övertoner härleds för de symmetriska komponenterna. Den högfrekventa signalinjektionen utförs genom att addera en liten högfrekvent trefasspänning till den matningsspänningen. Den resulterande högfrekventa negativa strömkomponenten kan användas som en tillförlitlig indikator för att upptäcka eventuella kortslutningar i statorlindningen. Förmågan som felindikator hos den högfrekventa negativa sekvensströmmen jämförs med den grundläggande negativa strömkomponentens förmåga, vilken är den traditionella indikatorn för att detektera dessa fel. Den högfrekventa signalinjiceringsmetoden som föreslås i detta arbete undersöks experimentellt på en prototypmaskin. Den föreslagna felindikatorn har visat sig vara fördelaktig jämfört med användningen av den traditionella felindikatorn för frekvensomriktare. I synnerhet visas att den föreslagna felindikatorn är mindre beroende av frekvensomriktarens driftsförhållanden än den traditionella felindikatorn.

Condition Monitoring (CM) is widely used in industry to meet sustainability, safety, and equipment efficiency requirements. Intelligent Fault Diagnosis (IFD) research focuses on automating CM data analysis tasks, to detect and prevent machine faults, and provide decision support. IFD enables trained analysts to focus their efforts on advanced tasks such as fault severity estimation and preventive maintenance optimization, instead of performing routine tasks. Industry datasets are rarely labelled, and IFD models are therefore typically trained on labelled data generated in laboratory environments with artificial or accelerated fault development. In the process industry, fault characteristics are often context-dependent and difficult to predict in sufficient detail due to the heterogeneous environment of machine parts. Furthermore, fault development is non-linear and measurements are subject to varying background noise. Thus, IFD models trained on lab data are not expected to transfer well to process industry environments, and require on-site pre-training or fine-tuning to facilitate accurate and advanced fault diagnosis. While ground truth labels are absent in industrial CM datasets, analysts sometimes write annotations of faults and maintenance work orders that describe the fault characteristics and required actions. These annotations deviate from typical natural language due to the technical language used, characterised by a high frequency of technical terms and abbreviations. Recent advances in natural language processing have enabled simultaneous learning from unlabelled pairs of images and captions through Natural Language Supervision (NLS). In this thesis, opportunities to enable weakly supervised IFD using annotated but otherwise unlabelled CM data are investigated. This thesis proposes novel machine learning methods for joint representation learning for IFD directly on annotated CM data. The main contributions are: (1) the introduction and implementation of technical language supervision to merge advances in natural language processing and, including a literature survey; (2) the creation of a method to improve technical languageprocessing by substituting out-of-vocabulary technical words with natural language descriptions, and to evaluate language model performance without explicit labels or downstream tasks; (3) the creation of a method for small-data language-based fault classification using human-centricvisualisation and clustering. Preliminary results for sensor and cable fault detection show an accuracy of over 90%. These results imply a considerable increase in the value of annotated CM datasets through the implementation of IFD models directly on industry data, e.g. for improving the decision support to avoid unplanned stops. / KnowIT FAST