Abstract
In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.
Highlights
Nowadays we are living a digital transformation in maintenance and asset management, characterized by higher assets’ performance information levels and enhanced management control possibilities, in real time and throughout the useful life of the assets.Data is what really enables this transformation and is becoming critical in today’s organizations [1].Having useful data available is becoming paramount in order to be able to make the best decisions in Asset Management (AM)
We propose a classification of the Data Mining (DM) techniques, differentiating among models requiring or not a reference value for the analyzed variable
The approach proposed in the current paper is the opposite: the normal behavior is firstly estimated through an Artificial Neural Network (ANN) and, when a substantial deviation is noticed, the Association Rule (AR) mining is applied to diagnose whether such a deviation impacts on the efficiency and performance of the asset
Summary
Nowadays we are living a digital transformation in maintenance and asset management, characterized by higher assets’ performance information levels and enhanced management control possibilities, in real time and throughout the useful life of the assets. The basic concept is the need for a common asset database to provide a primary reference to information regarding assets’ properties for supporting decisions [5] Another key development to improve data quality and long-term availability is the emergence of digital technologies and their adoption in AM. We need practical tools that can be adapted to any potential current asset location and operating condition, having the possibility to control asset performance and reliability, ensuring life cycle expectations according to existing business plans This has become an important concern for many industries and utilities with very intensive capitalization in long-lasting assets [27]. We present some practical results, conclusions and further research opportunities
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