Abstract
In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.
Highlights
In the last century, a new notion called “Industry 4.0” has emerged, in which the world has witnessed an industrial and technological revolution that caused the rapid spread of complex sensor systems in various domains and applications
The proposed system scenario represents a possible method to merge random forest as a datadriven approach for industrial Fault Detection and Diagnosis (FDD) and model-based FDD approaches into a final hybrid approach, which possesses the powerful features of both approaches
Random Forest (RF) is used following the steps in the data-driven flowchart in Figure 1, to generate dynamic diagnostic rules to diagnose and monitor the health of a hydraulic test rig
Summary
A new notion called “Industry 4.0” has emerged, in which the world has witnessed an industrial and technological revolution that caused the rapid spread of complex sensor systems in various domains and applications. These systems are literally everywhere; in aircrafts, transportation like cars and ships, computing systems such as computers, laptops, smart phones, embedded systems, and in many industrial applications such as factories, chemical reactors and nuclear power plants and many more countless examples. Faults consequences fall in a spectrum that ranges from harmless, ignorable faults to extremely disastrous ones that may lead to major economical and human catastrophes
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