Introduction to Total Productive Maintenance (TPM)

Yogendra Sagar Mishra

The first lab tripped me up a bit: the data ingest assumes you’ve already got a sensor stream cleaned and timestamped, which wasn’t spelled out. After that, it stayed grounded in real constraints, not toy math. The section on envelope analysis stuck, especially the bearing fault example where they compared raw FFT vs filtered bands and showed how false positives creep in at low RPS. I liked the framing around arch tradeoffs—where CBM logic lives vs infra—and the quick nod to wiring it into CI without overthinking prod. It’s beginner-friendly without talking down, and I’ve already caught myself rethinking how we flag drift in obs for our k8s workloads. Feels like I’m past a small plateau now.

Ved Naik Engineering

This mapped pretty closely to the kind of PRs I’m skimming between standups, just framed around physical equipment instead of code. The intermediate level felt right; it assumes you know the basics and jumps into how maintenance decisions play out in prod-like conditions. The bit that stuck was the section on condition-based maintenance, specifically the example where a bearing’s vibration trend crosses the alert threshold but temp stays flat, and how they decide not to intervene yet. some of the early safety refreshers were a bit slow if you’ve worked around equipment before. Still, tying failure modes back to monitoring and obs habits made it easy to relate to infra work and energy utilities contexts. I wasn’t sold on the checklist format in Chapter 2, but the later edge cases around false positives and deferred fixes are where it separates itself.

Muhammad Hussain

Initially, I wasn’t sure what to expect from this course. Process control is something that shows up everywhere on site, but the theory behind it had always been a bit fragmented for me. The sections on open-loop vs. closed-loop control helped close that gap, especially when tied to real examples like distillation column temperature control in chemical/pharmaceutical plants and boiler drum level control in energy utilities. One area that stood out was how feedback control behaves under disturbances. That directly connects to issues seen on an oil & gas separator pressure loop I’ve worked on, where load changes kept throwing the controller off. A challenge during the course was translating the block diagrams into what actually happens in the DCS screens, especially when multiple control objectives conflict. It took a bit of effort to map theory to noisy plant data. A practical takeaway was learning a more structured way to decide whether a loop even needs tight closed-loop control or if a simpler approach is acceptable. That alone will save time during commissioning and troubleshooting. The content feels immediately usable, and I can see this being useful in long-term project work.

Nupurkumar Prajapati supervisor

This course turned out to be more technical than I anticipated. The coverage of open-loop versus closed-loop control was straightforward, but the real value came from how those ideas were tied to actual industrial examples. The sections on PID control and feedback loops lined up well with issues seen on chemical and pharmaceutical projects, especially around reactor temperature control and maintaining consistent product quality. Examples around distillation column control also felt familiar from oil and gas work, where small tuning errors can ripple through the whole unit. One challenge was mentally translating the clean block diagrams into what actually happens in a live DCS environment, with noisy signals and slow valves. The course didn’t hide that gap, which was helpful, but it did take some effort to connect theory to practice. A practical takeaway was a clearer approach to choosing control strategies and tuning priorities, especially balancing stability versus responsiveness. That’s already been useful on an energy utilities project dealing with boiler feedwater control. Overall, it felt grounded in real engineering practice.