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|Document Type: ||Thesis or dissertation|
|Title: ||An online-integrated condition monitoring and prognostics framework for rotating equipment|
|Authors: ||Alrabady, Linda Antoun Yousef|
|Supervisors: ||Mba, David|
|Issue Date: ||Oct-2014|
|Abstract: ||Detecting abnormal operating conditions, which will lead to faults developing
later, has important economic implications for industries trying to meet their
performance and production goals. It is unacceptable to wait for failures that
have potential safety, environmental and financial consequences. Moving from
a “reactive” strategy to a “proactive” strategy can improve critical equipment
reliability and availability while constraining maintenance costs, reducing
production deferrals, decreasing the need for spare parts. Once the fault
initiates, predicting its progression and deterioration can enable timely
interventions without risk to personnel safety or to equipment integrity.
This work presents an online-integrated condition monitoring and prognostics
framework that addresses the above issues holistically. The proposed
framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F
curve. Depending upon the running state of machine with respect to its I-P and
P-F curve an algorithm will do one of the following:
(1) Predict the ideal behaviour and any departure from the normal operating
envelope using a combination of Evolving Clustering Method (ECM), a
normalised fuzzy weighted distance and tracking signal method.
(2) Identify the cause of the departure through an automated diagnostics
system using a modified version of ECM for classification.
(3) Predict the short-term progression of fault using a modified version of the
Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here
MDENFIS and a tracking signal method.
(4) Predict the long term progression of fault (Prognostics) using a combination
of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode
Decomposition (EMD) for predicting the future input values and MDENFIS for
predicting the long term progression of fault (output).
The proposed model was tested and compared against other models in the
literature using benchmarks and field data. This work demonstrates four
noticeable improvements over previous methods:
(1) Enhanced testing prediction accuracy, (2) comparable processing time if not
better, (3) the ability to detect sudden changes in the process and finally (4) the
ability to identify and isolate the problem source with high accuracy.|
|Appears in Collections:||PhD and Masters by research theses (School of Engineering)|
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