AI Based Degradation Trend Estimation of Aerial Bundle Cable For Efficient Preventive Maintenance

Aerial Bundle Cables, often referred to as Aerial Bundled Conductors or simply ABC, are cables used in overhead electrical utility power supply lines. With the insulation of the cables, the ABCs offer better protection from electrical pilferage; subsequently, these cables are of interest to metropol

Project Title

AI Based Degradation Trend Estimation of Aerial Bundle Cable For Efficient Preventive Maintenance

Project Area of Specialization

Artificial Intelligence

Project Summary

Aerial Bundle Cables, often referred to as Aerial Bundled Conductors or simply ABC, are cables used in overhead electrical utility power supply lines. With the insulation of the cables, the ABCs offer better protection from electrical pilferage; subsequently, these cables are of interest to metropolitan areas with electrical pilferage problems. Lately, the ABCs have been utilized in Karachi metropolitan by M/s K-Electric, instead of the standard copper cables to prevent electricity from theft.

Nonetheless, because of relevantly new and infrequent use of the cables in Karachi, the life and performance of these Aerial Bundle Cables isn’t known for Karachi; which has a harsh climate due to being on the coastal line. The identification of the degradation rate of the ABCs is of great interest with reference to various factors, such as state of the environment, thermal, moisture, mechanical loading conditions etc. The degradation trend information enables the prediction of the remaining useful life (RUL) of the ABCs. This research work, Final Year Project (FYP) focuses on degradation trend prediction of ABCs using sophisticated Artificial Intelligence (AI) Schemes.

The implementation of suitable non-destructive testing (NDT) techniques is beneficial for the RUL prediction to assess the cable condition and in turn degradation rates of the cables. The NDT and environmental data will enable degradation trend prediction through maintaining a historical database. Sophisticated signal processing and AI based algorithms will help predict degradation trend estimation of the ABCs. In this FYP, we will use and investigate various AI and statistical based prediction schemes for degradation trend prediction, such as linear and Polynomial Regression, Gaussian and Deep Gaussian processes, Artificial Neural Networks (ANN), Long Short Term Memory (LSTM).

Project Objectives

1. Feature selection: Finalization/Selection of parameter(s) to assess/measure Degradation state
2. Framework development: Development of framework for prognosis/prediction of Degradation trend
3. Prediction of the future States: AI based Degradation trend estimation

Project Implementation Method

Aerial Bundle Cables (ABCs) are subjected to degradation due to various reasons, such as harsh environmental conditions including moisture, thermal, mechanical, and electrical loading conditions. Such degradation reduces the operational efficiency and life of the cables, which is a challenge for the power distribution industry and the associated maintenance managers. Condition monitoring offers a solution to this challenge as it enables cable health assessment and degradation state estimation.

NDT techniques will facilitate condition health estimation. Suitable/appropriate degradation parameter selection helps in efficient and distinctive health/ condition assessment. Candidate parameters are as given: We will select a suitable degradation parameter on the basis of literature review and field survey. Also, Historical Database generation (periodic recording/ listing of Thermal Degradation Parameter/ Selected degradation parameter).

This will lead us in the estimation of the degradation trend. Various prediction techniques can be applied for prediction of this degradation trend, these include data-driven, physical model-based, statistical and other approaches. Sophisticated AI based techniques will be used, is a manner to organize and use the knowledge efficiently in such a way that It should be perceivable by the people who provide it. This FYP focuses on implementing efficient AI based schemes for degradation trend estimation. This enables RUL estimation. This can be used by maintenance managers to efficiently plan maintenance activities.

Benefits of the Project

• Accurate degradation trend prediction allows efficient planning of maintenance activities and regimes.
• Effective degradation prognosis ensures upkeep, service availability and longevity of the asset.
• It also reduces the probability of failure.
• Reduction in system downtime over the lifespan of the asset.
• Historical Database along with AI capabilities enables additional benefits of Sessional trend estimation and anomaly detection.

Technical Details of Final Deliverable

1) Historical Database:
         A dataset containing 6-12 months data, the data would comprise of finalize degradation paramenter(s) values of each month data.

2) AI-based Prognostic framework (Degradation trend estimation):
       Degradation trend estimation results. Test result and accurate prediction on the basis of historical data. Results may be reported in the form of reports and GUIs.

Final Deliverable of the Project

Software System

Core Industry

Energy

Other Industries

IT

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Affordable and Clean Energy, Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Sustainable Cities and Communities, Responsible Consumption and Production, Life on Land

Required Resources

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