Many predictive maintenance projects fail because the data was never good enough, not because the model was weak. I start by checking what your data can really show: sampling rate, gaps, noise, and timing. Then I build a model that fits it. I won’t stop there. I shall validate against physics of failure as applicable as plant machineries are engineered systems with specific failure modes unlike BFSI and IT cases I have 25+ years in plant automation and hands-on experience building anomaly detection on real PLC and sensor data. You get honest advice, clean documented Python code that you own, and a plain-language report. Fault prediction and remaining useful-life estimates need a history of past failures. I will tell you upfront if your data can support them. Anonymized data is fine.