One focus of the Industry 4.0 paradigm is to enable Smart Factories with improved productivity and reduced down-times. In this context, Predictive Maintenance (PM) is a proactive approach to industrial services that optimises maintenance actions based on the system’s health. In order to monitor and understand the system’s status, effective PM requires dedicated tools capable of managing a large amount of data and discern the right data set required for analysis. As an aid for engineers, the software called MADe can be used. MADe is a model-based platform that can optimise maintenance actions following the information provided by the software itself, concerning sensor selection and functional models. In particular, among many others, MADe incorporates functionalities for incipient fault detection, which may be extremely useful when monitoring systems comprising fatigue or aging sensitive components. In fact, early fault detection enables scheduling of maintenance that will minimise the impact on production outputs. Owing to these considerations, this paper describes a technique for detection of incipient faults components affected by fatigue using an Equivalent Damage Index (EDI). This technique is tested on data taken from the literature in order to verify its potentials.
In the context of Industry 4.0, Condition Based Maintenance (CBM) for complex systems is essential in order to identify failures and mitigate them. After the identification of a sensor set that guarantees the system monitoring, three main problems must be addressed for effective CBM: i) collection of the right data; ii) choice of the optimal technique to identify the specific data-set; iii) correct classification of the results. The solutions currently used are typically data driven and, therefore, the results are variable, as it is sometimes challenging to identify a pattern for all specific failures. This paper presents a solution that combines a data driven approach with an in-depth knowledge of the mechanical system’s behaviour. The choice of the right sensor set is calculated with the aid of the software MADe (Maintenance Aware Design environment), whereas the optimal data-set identification technique is pursued with a second tool called Syndrome Diagnostics. After an overview of such methodology, this work also presents RSGWPT (Redundant Second Generation Wavelet Packaged Transform) analysis to show different possible outcomes depending on the available sensor data and to tailor a detection technique to a given data set. Supervised and unsupervised learning techniques are tested to obtain either an anomaly detection or a failure identification depending on the chosen sensor set. By using the described method, it is possible to identify potential failures in the system with sufficient notice to implement the optimal maintenance actions.
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