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Predictive Maintenance in HVAC Industry

Submitted by Arnab Biswas (@arnabbiswas) on Nov 22, 2020

Format: 40 min talk Status: Submitted

Abstract

Heating, Ventilation, Air Conditioning (HVAC) equipment plays a very critical role in commercial buildings by maintaining thermal comfort and indoor air quality. The performance of a HVAC equipment becomes prominent when it malfunctions. So, maintenance is a major focus area for HVAC owners and operators. Maintenance is done either by replacing parts at regular intervals even when those are working (Preventive Maintenance) or by replacing the parts only when there is failure (Reactive Maintenance). Predictive Maintenance avoids the drawbacks of Preventive Maintenance (under utilization of a part’s life) and Reactive Maintenance (unscheduled downtime). Based on the health of an equipment in the past, future point of failure can be predicted in Predictive Maintenance. Thus, replacement of parts can be scheduled just before the actual failure. Traditionally, predictive maintenance is being done using rule based techniques. With the advent of connected sensors (IoT), data from HVAC equipment is continuously collected and fed to Machine Learning based systems to predict its future health.
In this session, I am going to discuss how the HVAC industry is utilizing Machine Learning for the purpose of Predictive Maintenance.

Outline

  • What is HVAC?
  • Types of Maintenance in HVAC Industry
  • Predictive Maintenance: Definition, Benefit & Goals
  • End to End Architecture of Predictive Maintenance System
  • Data Science Life Cycle for Machine Learning based Predictive Maintenance
    • Conversion of Business Problem into Machine Learning Problem
    • Collection of Relevant Data (HVAC Health, Maintenance, Asset Metadata)
    • Aggregation of Data from different sources
    • Data Preprocessing
    • Feature Engineering specific to HVAC Health & Maintenance Data (Time Series)
    • Selection of relevant Cross Validation technique
    • Selection of Algorithms based on nature of the data & maturity of Machine Learning Pipeline
    • Model Deployment: Batch Scoring
    • Monitoring & Maintenance of the model

Requirements

Knowledge about fundementals of Machine Learning

Speaker bio

Arnab Biswas is a Data Scientist working in EcoEnergy, Carrier Global Corporation. He has 15 years of experience in Software Development in Telecom, Networking & HVAC industry. As a developer, he has developed highly scalable, distributed, fault tolerant softwares to manage/provision IoT devices. In Machine Learning, his current area of focus is Predictive Maintenance for HVAC Equipments using Big Data. As a Core Team member of DataKind Bangalore, he has volunteered for different Non Profit Organizations in India helping them address their Data Science related needs. He contributes to StackOverflow and different Open Source Softwares.

Links

Slides

https://speakerdeck.com/arnabbiswas1/predictive-maintenance-in-hvac-industry

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