Developing Predictive Maintenance Model Based on Fault Diagnosis Technique for Miniature Sewage Treatment Unit

EasyChair Preprint 10664

Abstract

    One of the best strategies to decrease unexpected downtime, save maintenance costs, and increase asset longevity is to monitor mechanical components of equipment. Given that most failures can be prevented, condition monitoring is a key part of predictive maintenance and is thus more effective than corrective maintenance. The process of condition monitoring includes fault diagnosis, which enables operators to not only identify mechanical anomalies but also to identify the underlying source of the problem and make the necessary repairs. In order to detect and identify flaws, a fault diagnostic system based on predictive maintenance approach has been built in this work. The developed model has been used to monitor and diagnose the operational behavior of a mechanical aeration plant. Arduino, sensors (temperature and vibration sensors), and PC software have been used as the system's major building blocks for the suggested system's predictive maintenance plan. Using the Arduino programming language and LabVIEW software, online data collecting, signal analysis, defect diagnosis, and results presentation activities were written and simulated. According to their severity, fault representation types are categorized into (N, C, Ci, Cs, and D) levels based on vibration and temperature measurements. Based on the online data from the case study used in the defects identification model, the findings determine whether a fault occurred or not. They also analyze the potential causes of the faults so that the proper remedial action may be done. The fault-diagnostic system's adoption has led to improved decision-making that helps to update the maintenance plan, which helps to lower overall maintenance costs and downtime for all equipment.

Keyphrases: Predictive Maintenance, Preventive Maintenance, fault diagnosis

@booklet{EasyChair:10664,
  author    = {Amjad Barzan and Hasanain Alaa},
  title     = {Developing Predictive Maintenance Model Based on Fault Diagnosis Technique for Miniature Sewage Treatment Unit},
  howpublished = {EasyChair Preprint 10664},
  year      = {EasyChair, 2023}}