The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods. In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems. The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability

Computational data mining is of interest in the pharmaceutical arena for the analysis of massive amounts of data and to assist in the management and utilization of the data. In this study, a data mining approach was used to predict the miscibility of a drug and several excipients, using Hansen solubility parameters (HSPs) as the data set. The K-means clustering algorithm was applied to predict the miscibility of indomethacin with a set of more than 30 compounds based on their partial solubility parameters [dispersion forces , polar forces and hydrogen bonding ]. The miscibility of the compounds was determined experimentally, using differential scanning calorimetry (DSC), in a separate study. The results of the K-means algorithm and DSC were compared to evaluate the K-means clustering prediction performance using the HSPs three-dimensional parameters, the two-dimensional parameters such as volume-dependent solubility and hydrogen bonding , and selected single (one-dimensional) parameters. Using HSPs, the prediction of miscibility by the K-means algorithm correlated well with the DSC results, with an overall accuracy of 94%. The prediction accuracy was the same (94%) when the two-dimensional parameters or the hydrogen-bonding (one-dimensional) parameter were used. The hydrogen-bonding parameter was thus a determining factor in predicting miscibility in such set of compounds, whereas the dispersive and polar parameters had only a weak correlation. The results show that data mining approach is a valuable tool for predicting drug–excipient miscibility because it is easy to use, is time and cost-effective, and is material sparing.

The paper addresses the potential use of cloud services in Functional Products (FP) and its possible implications for availability. Further, how the implications for availability can be understood via modelling and simulation is addressed. The paper adds further specificity to literature by indicating the FP constituents for which cloud services are applicable and adequate.

Improving product quality is always of industrial interest. Product availability, a function of product maintainability and reliability, is an example of a measurement that can be used to evaluate product quality. Product availability and cost are two units which are especially important to manage in the context of the manufacturing industry, especially where industry is interested in selling or buying offers with increased service content. Industry in general uses different strategies for increasing equipment availability; these include: corrective (immediate or delayed) and preventive strategies. Preventive strategies may be further subdivided into scheduled and predictive (condition-based) maintenance strategies. In turn, predictive maintenance may also be subdivided into scheduled inspection and continuously monitored. The predictive approach can be achieved by early fault detection. Fault detection and diagnosis methods can be classified into three categories: data-driven, analytically based, and knowledge-based methods. In this thesis, the focus is mainly on fault detection and on data-driven models.Furthermore, industry is generating an ever-increasing amount of data, which may eventually become impractical to store and search, and when the data rate is increasing, eventually impossible to store. The ever-increasing amount of data has prompted both industry and researchers to find systems and tools which can control the data on the fly, as close to real-time as possible, without the need to store the data itself. Approaches and tools such as Data Stream Mining (DSM) and Data Stream Management Systems (DSMS) become important. For the work reported in this thesis, DSMS and DSM have been used to control, manage and search data streams, with the purpose of supporting increased availability of industrial products.Bosch Rexroth Mellansel AB (formerly Hägglunds Drives AB) has been the industrial partner company during the course of the work reported in this thesis. Related data collection concerning the functionality of the BRMAB hydraulic system has been performed in collaboration with other researchers in Computer Aided Design at Luleå University of Technology.The research reported in this thesis started with a review of data stream mining algorithms and their applications in monitoring. Based on the review, a data stream classification method, i.e. Grid-based classifier, was proposed, tested and validated (Paper A). Also, a fault detection system based on DSM and DSMS was proposed and tested, as reported in Paper A. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus demonstrating how data stream prediction can be used to gain more time for proactive response actions by industry (Paper B). Further development included an automatic update method which allows the proposed fault detection system to be able to overcome the problem of concept drift (Paper E). The proposed and modified fault detection systems were tested and verified using data collected in collaboration with Bosch Rexroth Mellansel AB (BRMAB). The requirements for the proposed fault detection system and how it can be used in product development and design of the support system were also discussed (Paper C). In addition, the performance of a knowledge-based method and a data- driven method for detecting failures in high-volume data streams from industrial equipment have been compared (Paper D). It was found that both methods were able to detect all faults without any false alert. Finally, the possible implications of using cloud services for supporting industrial availability are discussed in Paper F. Further discussions regarding the research process and the relations between the appended papers can be found in Chapter 2, Figure 4 and in Chapter 5, Figure 21.The results showed that the proposed and modified fault detection systems achieved good performance in detecting and predicting failures on time (see Paper A and Paper B). In Paper C, it is shown how data stream management systems may be used to increase product availability awareness. Also, both the data-driven method and the knowledgebased method were suitable for searching data streams (see Paper D). Paper E shows how the challenge of concept drift, i.e. the situation in which the statistical properties of a data stream change over time, was turned to an advantage, since the authors were able to develop a method to automatically update the safe operation limits of the one-class data-driven models.In general, detecting faults and failures on time prevents unplanned stops and may improve both maintainability and reliability of industrial systems and, thus, their availability (since availability is a function of maintainability and reliability). By the results, this thesis demonstrates how DSM and DSMS technologies can be used to increase product availability and thereby increase product quality in terms of availability.

Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing.In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM).The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.

Functional Products (FP) and Product Service Systems (PSS) may be seen as integrated systems comprising hardware and support services. For such offerings, availability is key. Little research has been done on integrating Data Stream Management Systems (DSMS) for monitoring (parts of) a FP to improve system availability. This paper introduces an approach for how data stream mining may be applied to monitor hardware being part of a Functional Product. The result shows that DSMS have the potential to significantly support continuous availability awareness of industrial systems, especially important when the supplier is to supply a function with certain availability.

Products of high quality are of great interest for industrial companies. The quality of a product can be considered in terms of production cost, operating cost, safety and product availability, for example. Product availability is a function of maintainability and reliability. Monitoring prevents unplanned stops, thus increasing product availability by decreasing needed maintenance. Through monitoring, failures can be detected and/or avoided. Detecting failures eliminates extra costs such as costs associated with machinery damage and dissatisfied customers, and time is saved since stops can be scheduled, instead of having unplanned stops. Product monitoring can be done through searching the data generated from sensors installed on products.Nowadays, the data can be collected at high rates as part of a data stream. Therefore, data stream management systems (DSMS) and data stream mining (DSM) are being used to control, manage and search the data stream. This work investigated how the availability of industrial products can be increased through the use of DSM and DSMS technologies.A review of the data stream mining algorithms and their applications in monitoring was conducted. Based on the review, a new data stream classification method, i.e. Grid-based classifier was proposed, tested and validated. Also, a fault detection system based on DSM and DSMS technologies was proposed. The proposed fault detection system was tested using data collected from Hägglunds Drives AB (HDAB) hydraulic motors. Thereafter, a data stream predictor was integrated into the proposed fault detection system to detect failures earlier, thus gaining more time for response actions. The modified fault detection system was tested and showed good performance. The results showed that the proposed fault detection system, which is based on DSM and DSMS technologies, achieved good performance (with classification accuracy around 95%) in detecting failures on time. Detecting failures on time prevents unplanned stops and may improve the maintainability of the industrial systems and, thus, their availability.

Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data.The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental.The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated.

This paper reviews, analyses, discusses and summarises the recent research and development and trends in the applications of soft computing in the field of wind power systems. We show the usage and the influence of soft computing on the different aspects of wind power systems especially in the field of operation and maintenance. This work provides the state of the art in this area which will be a good guidance for future research work. The main results achieved from the study show that the soft computing techniques are adequate for solving the different challenges at the different phases of the life cycle processes of wind power systems. Using the various soft computing techniques with wind power systems proved to be useful for the wind energy business. Using these tools contribute by improving the robustness of the decisions at different phases of the system's life cycle. Soft computing can enhance the efficiency and effectiveness of the operation and maintenance of offshore wind power systems through improving the availability levels. Thus, providing secure, sustainable and competitive energy supply for the future.