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Need Machine Learning Expert (Random Forest + SVM) for Electrical Engineering Thesis (Predictive Maintenance)
예산
$250~$750 AUD
예상 기간
1~2개월
난이도
전문가
기술 스택
Python
scikit-learn
pandas
numpy
PuLP
OR-Tools
Jupyter Notebook
Machine Learning
AI
Predictive Maintenance
Random Forest
SVM
Decision Tree
Data Analysis
Feature Engineering
Hyperparameter Tuning
Cross-validation
Optimization Algorithms
Monte Carlo Simulation
Electrical Engineering
Data Science
AI 분석 요약
전력 시스템 분야 석사 논문을 위해 머신러닝 기반 예측 유지보수 프레임워크를 개발하는 프로젝트입니다. Random Forest, SVM 등 ML 모델 구현 및 튜닝, 데이터 처리(결측치, 불균형, 특성 공학), 합성 데이터 생성, 최적화 기반 유지보수 스케줄링 개발 및 분석 역량이 필요합니다.
프로젝트 원문 설명
am currently working on my Master’s thesis in Electrical Engineering (Power Systems) and I am looking for an experienced freelancer to assist with the technical implementation.
The project focuses on developing an AI-based predictive maintenance framework for distribution transformers, combining machine learning models with optimization-based maintenance scheduling.
Scope of Work:
1. Machine Learning Models
Implement and refine:
Random Forest (partially completed)
Support Vector Machine (RBF kernel)
Decision Tree (for benchmarking if needed)
Perform:
Hyperparameter tuning (GridSearchCV or RandomizedSearchCV)
5-fold cross-validation
Model evaluation (Accuracy, F1-score, AUC)
2. Data Handling and Feature Engineering
Work with BRAVO dataset (15,000+ samples, 16 features)
Handle:
Missing data
Class imbalance (SMOTE already applied)
Feature engineering:
Load Stress
Maintenance Overdue
Failure Risk Score
Criticality Index
3. Synthetic Data Generation
Generate additional fault scenarios using Monte Carlo simulation
Based on DGA gas ratios (IEC standards)
Validate synthetic data (e.g., KS test)
4. Optimization and Simulation
Develop maintenance scheduling models:
Baseline approach
Greedy algorithm
Integer Programming (PuLP or similar)
Integrate machine learning predictions into scheduling decisions
5. Analysis and Results
Compare AI-based maintenance with traditional methods
Perform cost-benefit analysis (target improvement: 10–15%)
Conduct sensitivity analysis
Technical Requirements:
Python (mandatory)
scikit-learn, pandas, numpy
Optimization libraries (PuLP or OR-Tools)
Jupyter Notebook
Experience in predictive maintenance or engineering datasets is preferred
Deliverables:
Clean, well-documented Python code
Model outputs and evaluation metrics
Simulation results (graphs and comparisons)
Brief explanation of methodology for thesis use
The project focuses on developing an AI-based predictive maintenance framework for distribution transformers, combining machine learning models with optimization-based maintenance scheduling.
Scope of Work:
1. Machine Learning Models
Implement and refine:
Random Forest (partially completed)
Support Vector Machine (RBF kernel)
Decision Tree (for benchmarking if needed)
Perform:
Hyperparameter tuning (GridSearchCV or RandomizedSearchCV)
5-fold cross-validation
Model evaluation (Accuracy, F1-score, AUC)
2. Data Handling and Feature Engineering
Work with BRAVO dataset (15,000+ samples, 16 features)
Handle:
Missing data
Class imbalance (SMOTE already applied)
Feature engineering:
Load Stress
Maintenance Overdue
Failure Risk Score
Criticality Index
3. Synthetic Data Generation
Generate additional fault scenarios using Monte Carlo simulation
Based on DGA gas ratios (IEC standards)
Validate synthetic data (e.g., KS test)
4. Optimization and Simulation
Develop maintenance scheduling models:
Baseline approach
Greedy algorithm
Integer Programming (PuLP or similar)
Integrate machine learning predictions into scheduling decisions
5. Analysis and Results
Compare AI-based maintenance with traditional methods
Perform cost-benefit analysis (target improvement: 10–15%)
Conduct sensitivity analysis
Technical Requirements:
Python (mandatory)
scikit-learn, pandas, numpy
Optimization libraries (PuLP or OR-Tools)
Jupyter Notebook
Experience in predictive maintenance or engineering datasets is preferred
Deliverables:
Clean, well-documented Python code
Model outputs and evaluation metrics
Simulation results (graphs and comparisons)
Brief explanation of methodology for thesis use
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