Battery RUL Prediction
Data-driven Remaining Useful Life model for lithium-ion packs. Pipeline from raw signals → features → model → deployment.
About
Industrial Automation Engineer specializing in PLC platforms and control systems for industrial applications. Experienced in designing, integrating, and optimizing control logic to improve system reliability, performance, and usability. Background in power systems engineering, including transformer and cable sizing, load analysis, and generator specification for medium-voltage equipment. Applies data-driven techniques such as predictive modelling and degradation analysis to enhance system performance and support proactive maintenance strategies.
- QualificationB.Eng (Electrical) — University of Pretoria
- Current focusPLC control systems, industrial automation, predictive maintenance
- ToolsTIA Portal · MATLAB/Simulink · AutoCAD Electrical · Python · Excel
- InterestsPhilosophy of AI · systems design · technical storytelling
Skills & Expertise
Industrial Automation
PLC Programming (Siemens), HMI & SCADA
85%
Power Systems
Transmission Systems, Load Analysis
82%
Circuit Design
PCB design, Panel Design, motor control
80%
Data Analysis
Predictive Maintenance, Degradation Modelling
65%
Featured Projects
PV–Generator–Battery Microgrid
Developed a rule-based energy management system prioritizing solar PV to reduce diesel generator runtime by 22%. Built a cost-optimization model for multiple configurations.
Engineering Data Analysis – Battery RUL
Developed regression models predicting battery degradation with 89% accuracy using NASA datasets. Participated in Kaggle and Zindi ML competitions.
Let's Connect
Interested in collaborating or hiring? I prefer concise messages that state scope, timeline, and the key deliverable. A few bullet points goes a long way toward a fast response.
Direct: fannyfushayi75@gmail.com