Prognostic of complex machines and systems using partial least squares path modelling

As an engineering system becomes more complex and widespread, the inherent behaviour tends to become less obvious and difficult to quantify. Over the past decade, researchers have attempted to create ‘super-models’ in order to mimic the behaviour of a complex system. They hoped that these ‘super-models’ would eventually uncover important information and knowledge of the system. Unfortunately, many of these models were insufficient in robustness and offered very little predictive power over the entire lifespan of the machine. As a result, these state-of-the-art models are rarely implemented in industry.In order to bridge this knowledge gap, partial least squares (PLS) analysis is introduced as an innovative modelling technique in the mechanical and manufacturing engineering discipline. This research aims to develop a methodology to establish casual relationships between latent (unobservable) constructs embedded in complex systems. This research argues the importance of first utilising ‘soft data’ (expert knowledge) to establish the nature of the issue. By conducting experiments and obtaining ‘hard data’ (manifest observations), conceptual models are translated into scientific models that can be analysed using partial least squares path modelling (PLSPM). This research is supported by a world-leading medical manufacturer and supplier. The company’s intravenous bag production line has a performance issue from the overall equipment effectiveness (OEE) perspective. Using the methodology established in this research, a causal network is constructed for the production line. There are two general outcomes obtained using PLSPM. First, it identifies critical variables that offer a high degree of contribution in predicting the variation of the construct. Second, it establishes a causal network that maps out the relationships between low-level constructs (such as the operating environment) and high-level constructs (such as machine quality).There are two major conclusions drawn from this research. First, the casual relationship between expert’s knowledge and machine performance of complex system can be quantified using PLSPM. Expert’s knowledge has tangible meaning that can be translated into scientific models. Second, this research eliminates the use of ‘super-model’ while still being able to extract vital information on complex systems. Moreover, the success of this research suggests that the potential use of PLSPM in industry is limitless