This thesis presents the design and implementation of a process monitoring system for the
HIsarna pilot plant aimed at the early detection of anomalies, specifically foamers. Though
rare, foamers represent severe process faults that can significantly impact the plant’s safety
and reliability. Early detection of these events is crucial for maintaining operational stability
and steady iron production.

Given the ambiguous definitions of nominal and non-nominal operations in HIsarna, this thesis constructs a precise definition based on foamer characteristics. Foamers are non-nominal operations, while the absence of these foamer characteristics indicates nominal operations. The monitoring system utilizes data from a gas analysis sensor in the plant’s topside outlet, the dogleg. The dogleg gas analyser is chosen for its reliability and the absence of monitoring methods based on this sensor. To ensure the accuracy of the sensor data, an optimizationbased method is employed to eliminate air ingress disturbances.

An anomaly detection method based on distance measures is developed, incorporating the
HIsarna Operation Model (HIOM) to reduce sensitivity to operating point changes. Historical
process data is analyzed to establish a distance measure that reflects the correspondence of
current measurements to nominal operations. The performance of the anomaly detector is
evaluated based on the number of foamers detected, hours per false alarm, and detection
time.

The primary contribution of this thesis is the development of an anomaly detection algorithm
using Mahalanobis distance for the HIsarna pilot plant. This metric indicates how closely
current measurements match historical nominal operation data. Additional contributions include a rigorous definition of nominal and foaming operations, an optimization-based method for air ingress removal, and an observer for dogleg gas analysis data, which can estimate carbon conversion and oxygen flow prediction bias of HIOM.

This research presents the design and implementation of a fault diagnosis filter for a high-fidelity simulation model of the AB383 wire bonder. Fault diagnosis, which consists of detecting, isolating, and estimating faults, enables more effective maintenance strategies and potentially mitigates costly downtime in high-precision motion and positioning systems. When a system deviates from its expected behavior, it can be an indication of the presence of a fault within the system. Faults can occur as either multiplicative faults, arising from deviations in parameters within the system, or additive faults, resulting from external fault signals that impact the system’s operation. This study uses a model-based methodology that generates residuals that are subsequently analyzed using a regression method aimed at determining the influence of each fault in the residual signal, thereby facilitating fault estimation. A residual signal represents the difference between the actual system behavior and the expected behavior, mainly serving as an indicator of potential faults within the system. A data-driven threshold design is proposed to determine the detectability of faults. The main contributions of this work include the development of a fault modeling framework for the residual generation method and the application of the fault estimation framework to a linear high-fidelity simulation model affected by both multiplicative and additive faults. The simulation results demonstrate satisfactory performance, that is, accurately detecting and estimating faults. Moreover, the proposed method effectively identifies external disturbances and high levels of noise through power spectral density analysis. The findings highlight the potential of this approach, outperforming alternative methods in terms of accuracy when it comes to fault diagnosis for wire bonder machines. The research findings contribute to the active field of fault detection and estimation for complex systems, offering valuable insights for further studies and potential practical applications.