Hey there! That's a really insightful question that's becoming increasingly important as AI and PLC systems merge. When neural networks start making control decisions alongside traditional PLC logic, technicians need some new troubleshooting skills. Here's what you should focus on learning:

First, you'll need to understand the basics of how AI models work with PLCs. Unlike traditional deterministic logic, neural networks make probabilistic decisions based on patterns they've learned. When you see 'unexplainable' behavior, start by checking the input data quality - garbage in means garbage out, even for AI!

You should learn to use Explainable AI (XAI) tools that can show you why the neural network made certain decisions. These tools can highlight which input features were most influential in the AI's decision-making process. Also, get comfortable with monitoring the AI model's confidence scores - low confidence often indicates uncertain decisions that might lead to strange behavior.

Develop protocols for creating 'baseline comparisons' - run the same scenario through both the traditional PLC logic and the AI-enhanced system to spot discrepancies. Learn to maintain detailed logs of AI decisions alongside traditional PLC scan cycles, which will help you trace back when things go wrong.

Most importantly, technicians should be trained in 'graceful degradation' protocols - knowing how to safely switch back to traditional PLC control when the AI system produces unexplainable results. This ensures safety while you debug the neural network issues.

The key is blending traditional PLC troubleshooting skills with new AI debugging techniques. It's definitely a learning curve, but these skills will become essential as AI becomes more integrated into industrial automation!