Process Monitoring and Data Mining with Chemical Process Historical Databases

Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events

Intelligent Anomaly Detection of Machine Tools based on Mean Shift Clustering

For a fault detection of machine tools, fixed intervention thresholds are usually necessary. In order to provide an autonomous anomaly detection without the need for fixed limits, recurring patterns must be detected in the signal data. This paper presents an approach for online pattern recognition on NC Code based on mean shift clustering that will be matched with drive signals. The intelligent fault detection system learns individual intervention thresholds based on the prevailing machining patterns. Using a self-organizing map, data captured during the machine’s operation are assigned to a normal or malfunction state

Unsupervised Methods for Condition-Based Maintenance in Non-Stationary Operating Conditions

Maintenance and operation of modern dynamic engineering systems requires the use of robust maintenance strategies that are reliable under uncertainty. One such strategy is condition-based maintenance (CBM), in which maintenance actions are determined based on the current health of the system. The CBM framework integrates fault detection and forecasting in the form of degradation modeling to provide real-time reliability, as well as valuable insight towards the future health of the system. Coupled with a modern information platform such as Internet-of-Things (IoT), CBM can deliver these critical functionalities at scale. The increasingly complex design and operation of engineering systems has introduced novel problems to CBM. Characteristics of these systems - such as the unavailability of historical data, or highly dynamic operating behaviour - has rendered many existing solutions infeasible. These problems have motivated the development of new and self-sufficient - or in other words - unsupervised CBM solutions. The issue, however, is that many of the necessary methods required by such frameworks have yet to be proposed within the literature. Key gaps pertaining to the lack of suitable unsupervised approaches for the pre-processing of non-stationary vibration signals, parameter estimation for fault detection, and degradation threshold estimation, need to be addressed in order to achieve an effective implementation. The main objective of this thesis is to propose set of three novel approaches to address each of the aforementioned knowledge gaps. A non-parametric pre-processing and spectral analysis approach, termed spectral mean shift clustering (S-MSC) - which applies mean shift clustering (MSC) to the short time Fourier transform (STFT) power spectrum for simultaneous de-noising and extraction of time-varying harmonic components - is proposed for the autonomous analysis of non-stationary vibration signals. A second pre-processing approach, termed Gaussian mixture model operating state decomposition (GMM-OSD) - which uses GMMs to cluster multi-modal vibration signals by their respective, unknown operating states - is proposed to address multi-modal non-stationarity. Applied in conjunction with S-MSC, these two approaches form a robust and unsupervised pre-processing framework tailored to the types of signals found in modern engineering systems. The final approach proposed in this thesis is a degradation detection and fault prediction framework, termed the Bayesian one class support vector machine (B-OCSVM), which tackles the key knowledge gaps pertaining to unsupervised parameter and degradation threshold estimation by re-framing the traditional fault detection and degradation modeling problem as a degradation detection and fault prediction problem. Validation of the three aforementioned approaches is performed across a wide range of machinery vibration data sets and applications, including data obtained from two full-scale field pilots located at Toronto Pearson International Airport. The first of which is located on the gearbox of the LINK Automated People Mover (APM) train at Toronto Pearson International Airport; and, the second which is located on a subset of passenger boarding tunnel pre-conditioned air units (PCA) in Terminal 1 of Pearson airport. Results from validation found that the proposed pre-processing approaches and combined pre-processing framework provides a robust and computationally efficient and robust methodology for the analysis of non-stationary vibration signals in unsupervised CBM. Validation of the B-OCSVM framework showed that the proposed parameter estimation approaches enables the earlier detection of the degradation process compared to existing approaches, and the proposed degradation threshold provides a reasonable estimate of the fault manifestation point. Holistically, the approaches proposed in thesis provide a crucial step forward towards the effective implementation of unsupervised CBM in complex, modern engineering systems

Nonlinear fault detection and diagnosis using Kernel based techniques applied to a pilot distillation colomn

Fault detection and diagnosis is an important problem in process engineering. In this dissertation, use of multivariate techniques for fault detection and diagnosis is explored in the context of statistical process control. Principal component analysis and its extension, kernel principal component analysis, are proposed to extract features from process data. Kernel based methods have the ability to model nonlinear processes by forming higher dimensional representations of the data. Discriminant methods can be used to extend on feature extraction methods by increasing the isolation between different faults. This is shown to aid fault diagnosis. Linear and kernel discriminant analysis are proposed as fault diagnosis methods. Data from a pilot scale distillation column were used to explore the performance of the techniques. The models were trained with normal and faulty operating data. The models were tested with unseen and/or novel fault data. All the techniques demonstrated at least some fault detection and diagnosis ability. Linear PCA was particularly successful. This was mainly due to the ease of the training and the ability to relate the scores back to the input data. The attributes of these multivariate statistical techniques were compared to the goals of statistical process control and the desirable attributes of fault detection and diagnosis systems.Dissertation (MEng (Control Engineering))--University of Pretoria, 2008.Chemical EngineeringMEngUnrestricte

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

ADAPTIVE SAMPLING METHODS FOR TESTING AUTONOMOUS SYSTEMS

In this dissertation, I propose a software-in-the-loop testing architecture that uses adaptive sampling to generate test suites for intelligent systems based upon identifying transitions in high-level mission criteria. Simulation-based testing depends on the ability to intelligently create test-cases that reveal the greatest information about the performance of the system in the fewest number of runs. To this end, I focus on the discovery and analysis of performance boundaries. Locations in the testing space where a small change in the test configuration leads to large changes in the vehicle's behavior. These boundaries can be used to characterize the regions of stable performance and identify the critical factors that affect autonomous decision making software. By creating meta-models which predict the locations of these boundaries we can efficiently query the system and find informative test scenarios. These algorithms form the backbone of the Range Adversarial Planning Tool (RAPT): a software system used at naval testing facilities to identify the environmental triggers that will cause faults in the safety behavior of unmanned underwater vehicles (UUVs). This system was used to develop UUV field tests which were validated on a hardware platform at the Keyport Naval Testing Facility. The development of test cases from simulation to deployment in the field required new analytical tools. Tools that were capable of handling uncertainty in the vehicle's performance, and the ability to handle large datasets with high-dimensional outputs. This approach has also been applied to the generation of self-righting plans for unmanned ground vehicles (UGVs) using topological transition graphs. In order to create these graphs, I had to develop a set of manifold sampling and clustering algorithms which could identify paths through stable regions of the configuration space. Finally, I introduce an imitation learning approach for generating surrogate models of the target system's control policy. These surrogate agents can be used in place of the true autonomy to enable faster than real-time simulations. These novel tools for experimental design and behavioral modeling provide a new way of analyzing the performance of robotic and intelligent systems, and provide a designer with actionable feedback