That's a fascinating question about the 'lights-out factory' concept! You're right to wonder about the hidden pitfalls of 24/7 automated production. From what I've learned, there are several unexpected failure modes that traditional maintenance teams might not anticipate:

First, there's the 'cascading failure' problem. In traditional factories, humans often catch small issues before they snowball. But in a lights-out environment, a minor sensor glitch could trigger a chain reaction that shuts down the entire production line without anyone noticing until it's too late.

Second, there's the 'software aging' phenomenon. Unlike mechanical parts that wear predictably, software can develop subtle bugs or memory leaks over months of continuous operation that only manifest under specific, rare conditions.

Third, there's the 'interdependency blind spot.' Automated systems might work perfectly in isolation but fail when multiple systems interact in unexpected ways - like when a robot's positioning error causes a conveyor belt to jam, which then overloads a sorting system.

To design self-recovery systems, manufacturers are implementing several strategies:

1. **Predictive maintenance with AI** that learns normal operating patterns and can detect anomalies long before failure occurs

2. **Redundant systems with graceful degradation** - if one component fails, the system automatically switches to backup while continuing at reduced capacity

3. **Digital twins** that simulate the entire factory in real-time, allowing AI to test recovery strategies virtually before implementing them

4. **Autonomous root cause analysis** where AI agents diagnose problems and implement fixes without human intervention

The key insight is that lights-out factories need to be designed with 'resilience engineering' from the start - anticipating failures rather than just reacting to them. It's like creating a factory that can think for itself when things go wrong!