How Machine Learning Solved a Problem Faster Than a Mechanic
C. Pete Connor MS, CCCM
CX Strategy Leader | AI/ML Innovation for Customer Experience | Predictive Analytics Expert | Driving Revenue Growth Through Customer Retention
? Challenge: Electrical issues can be hard to diagnose, with faults like bad grounds or corroded wires causing system failures.
?? Traditional Troubleshooting: Usually involves manually testing fuses, checking voltage, and probing harnesses an inefficient process.
?? Innovative Solution: Developed a machine learning-powered assistant for efficient troubleshooting at 8:00 AM.
?? Training Data: AI model trained using Jeep Wrangler wiring diagrams, service manuals, relevant TSBs, and numerous real-world failure cases.
?? AI Insights: The AI provided a ranked list of failure points based on symptoms, identifying hidden patterns beyond traditional methods.
?? Unexpected Suggestion: At 8:30 AM, the AI proposed checking C1 and C3 at the TIPM for micro-fractures in the solder joints an overlooked potential issue.
The Fix: AI as an Electrical Engineer
?? Electrical Control Center: The Totally Integrated Power Module (TIPM) is responsible for distributing power throughout Jeeps.
?? System Vulnerability: A loss of contact in the TIPM can cause the entire system to shut down unpredictably.
?? AI Insight: AI identified micro-fractured solder joints as a cause for power loss without error codes in Jeep Wranglers.
?? Discovery: By 9:15 AM, disassembly revealed hairline fractures in the solder joints, just as predicted by AI.
??? Temperature and Vibration: The small cracks were problematic when heat expansion or vibration broke the connection.
?? Repair: By 10:00 AM, solder joints were reflowed to restore solid connections.
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?? Effective Fix: By 10:30 AM, the Jeep ran perfectly with no power loss, highlighting a fast, data driven solution
Machine Learning as a Mechanic’s Best Tool
?? Wrangler Repair: Proving machine learning goes beyond automation to augmentation.
??? Complex Systems Challenge: Biggest issue isn't skill deficit but instant access to all failure modes.
?? Human Limitations: Can't remember every failure mode, read all service bulletins, or process thousands of case studies in real-time.
?? Machine Learning Strength: Excels in pattern recognition and real-time case processing.
? Efficiency Boost: Electrical issue resolved in 2 hours instead of all-day ordeal.
?? Future of Troubleshooting: AI could revolutionize vehicle diagnosis and repair methods.
?? Predictive Detection: Real-time sensor data for predicting failures.
??? Automated ECU Tuning: Optimizes performance and efficiency.
?? Adaptive Models: Improve with each diagnosis.
?? ML Application Speed: Technology isn't the limit; application speed is.
?? Redefining Expertise: ML tackled an impossible problem today, with potential to redefine expertise tomorrow.
Hey Pete! Check out our Kickstarter for FPGA (Field Programmable Gate Arrays) for chips and boards.? ? Launch date: We're live!? ? Fipsy FPGA V2 focuses on educational content but can be used in all sorts of projects for beginners, educators, professionals, and hobbyists.? ? Link to Kickstarter:? ? https://www.kickstarter.com/projects/1013562009/fipsy-fpga-v2-the-breakout-board-for-beginners-and-creatives?ref=55gijy? ? Feel free to share it with others who are interested in learning and development!?