An Algorithm for Modelling Escalator Fixed Loss Energy for PHM and sustainable energy usage

TL;DR

This paper introduces a method to monitor escalator health by analyzing fixed loss energy using sensor data, enabling predictive maintenance and energy savings.

Contribution

It proposes a novel approach to compute and analyze escalator fixed loss energy for PHM, validated with experimental data and visualized through EWMA charts.

Findings

Effective detection of escalator health status through fixed loss energy analysis

Potential for reducing energy costs via proactive maintenance

Advantages and limitations of the proposed methods are discussed

Abstract

Prognostic Health Management (PHM) is designed to assess and monitor the health status of systems, anticipate the onset of potential failure, and prevent unplanned downtime. In recent decades, collecting massive amounts of real-time sensor data enabled condition monitoring (CM) and consequently, detection of abnormalities to support maintenance decision-making. Additionally, the utilization of PHM techniques can support energy sustainability efforts by optimizing energy usage and identifying opportunities for energy-saving measures. Escalators are efficient machines for transporting people and goods, and measuring energy consumption in time can facilitate PHM of escalators. Fixed loss energy, or no-load energy, of escalators denotes the energy consumption by an unloaded escalator. Fixed loss energy varies over time indicating varying operating conditions. In this paper, we propose to use escalators' fixed loss energy for PHM. We propose an approach to compute daily fixed loss energy based on energy consumption sensor data. The proposed approach is validated using a set of experimental data. The advantages and disadvantages of each approach are also presented, and recommendations are given. Finally, to illustrate PHM, we set up an EWMA chart for monitoring the fixed loss over time and demonstrate the potential in reducing energy costs associated with escalator operation.