---
title: "Predictive Maintenance"
description: "2 posts about Predictive Maintenance, summarised, each linking to the original."
---

# Predictive Maintenance
> 2 posts about Predictive Maintenance, summarised, each linking to the original.

## Articles

### [The turbine that tried to tell you it was failing](https://yomu.fyi/post/the-turbine-that-tried-to-tell-you-it-was-failing.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Caitlin Gordon
- Published: Apr 30, 2026

Gas turbines produce millions of daily readings—vibration, temperature, pressure, flow rates, and electrical output—but maintenance teams may learn about warning signals only when an unplanned outage occurs. The post argues that predictive maintenance has struggled less because of model capability than because decision-makers lack fluid access to model findings and operational context. Databricks Genie provides a conversational interface to sensor, maintenance, dispatch, cost, and regulatory data, allowing questions about elevated vibration against maintenance baselines and timing maintenance around outage cycles. It is presented as decision support, not automation, with time-series analysis, maintenance history, integrated costs, and compliance context helping asset managers investigate fleets and act with more confidence.


### [IoT in Manufacturing: Strategy, Components, Use Cases, and Challenges](https://yomu.fyi/post/iot-in-manufacturing-strategy-components-use-cases-and-challenges.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 22, 2026

IoT in manufacturing is presented as an operational architecture for collecting machine data, improving production and supply-chain visibility, and preventing equipment failures under strict latency, reliability, and safety requirements. The guide covers device selection, sensor placement, tiered sampling, edge-to-cloud pipelines, platform criteria, security, compliance, workforce training, and a phased deployment roadmap. Its technical split sends high-frequency vibration and acoustic signals to edge gateways for local processing while cloud systems support stateful analytics, predictive maintenance scoring, and model training; protocols such as MQTT, OPC-UA, AMQP, and Modbus are evaluation criteria. Reported examples include multi-factory OEE monitoring across more than 200 production lines, reducing reporting lag from 24 hours to under five minutes, digital twins for maintenance simulation, and IoT logistics routing. The action plan recommends starting with predictive maintenance and OEE monitoring on one line before scaling.