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Walking recovery talk : mental health organizational change
No full textThe full experience of mental illness cannot be described in isolation from the context in which one lives, yet the internal physical manifestation of symptoms has been the focus of treatment in western cultures. The “recovery” paradigm is emerging as best-practice philosophy for mental health practice and represents a significant departure from existing standards thereby challenging mental health organizations to re-negotiate their relationship with the dominant bio-medical model. Despite the growing acceptance of recovery philosophy, literature exploring large-scale recovery-oriented organizational change is sparse. The purpose of this research was twofold; 1) to outline the steps taken by change agents within an organization embarking on recovery organizational change, and 2) to understand the experience, including successes and challenges associated with change. The qualitative data obtained from interviewing seventeen participants revealed the impact of organizational contextual factors, leadership and communication on recovery organizational change. Further, the data exposed the complexity of challenging preconceptions and practice when trying to adopt recovery approaches. The findings may guide other community based mental health organizations in their recovery journey.Master's These
Model based fault detection for two-dimensional systems
No full textFault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial
systems. Extensive research has been carried out on FDI for one dimensional (1-D)
systems, where variables vary only with time. The existing FDI strategies are mainly focussed
on 1-D systems and can generally be classified as model based and process history data based
methods. In many industrial systems, the state variables change with space and time (e.g., sheet
forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter
systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented
by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems
represent a great challenge in both theoretical development and applications and only limited
research results are available.
In this thesis, model based fault detection strategies for 2-D systems have been investigated
based on the F-M and the Roesser models. A dead-beat observer based fault detection has been
available for the F-M model. In this work, an observer based fault detection strategy is investigated
for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique,
a dead-beat observer is developed and the state estimate from the observer is then input to a
residual generator to monitor occurrence of faults. An enhanced realization technique is combined
to achieve efficient fault detection with reduced computations. Simulation results indicate
that the proposed method is effective in detecting faults for systems without disturbances as well
as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems
but strict conditions are required in order for an observer and a residual generator to exist. These
strict conditions may not be satisfied for some systems. The effect of process noises are also not
considered in the observer based fault detection approaches for 2-D systems. To overcome the
disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation
error variances. Based on the state estimate from the Kalman filter, a residual is generated
reflecting fault information. A model is formulated for the relation of the residual with faults
over a moving evaluation window. Simulations are performed on two F-M models and results
indicate that faults can be detected effectively and efficiently using the Kalman filter based fault
detection.
In the observer based and Kalman filter based fault detection approaches, the residual signals
are used to determine whether a fault occurs. For systems with complicated fault information
and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault
detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component
analysis (DPCA). Based on historical data, the reference residuals are first generated using
either the observer or the Kalman filter based approach. Based on the residual time-lagged
data matrices for the reference data, the principal components are calculated and the threshold
value obtained. In online applications, the T2 value of the residual signals are compared with
the threshold value to determine fault occurrence. Simulation results show that applying DPCA
to evaluation of 2-D residuals is effective
