The AWI Analytics blog: condition monitoring, predictive maintenance and vibration analysis — diagnosis and the engineering that follows, written for UK SME maintenance and engineering teams working to cut unplanned downtime. Everyone else stops at the alert; these articles are about the rest of the loop. For how we work on the floor, see AWI Labs and the evidence behind it.
Lead times have moved this year and most stores lists have not. Three numbers and four decisions for every critical spare: what to hold on the shelf, what to order on warning, what to repair or exchange, and what you can afford to risk.
Read articleDetection is the cheap half. What a finding has to become before it can be scheduled — the scope of work, the parts lead time, who holds direction, who holds the spanner, and what stays with your team no matter who you hire.
Read articleYour machine didn’t fail the day it stopped — it had been failing for months, and it was telling you. The graph behind all of predictive maintenance: P, F, the warning window between them, and how much of it each detection method buys.
Read articleKnowing you were down 120 hours isn't knowing why. Uptime data is the scoreboard — condition monitoring is the layer beneath that explains the score, and the only one that can change it. The ladder: that → where → why → never again.
Read articleDetection has been commoditised. The value in reliability is everything that happens after the alert — diagnose the root cause, engineer it out, verify the fix. Why the market stops at half the job, and what closing the loop actually takes.
Read articleReliability is fluid reliability. What oil analysis reveals — wear metals, ISO 4406, contamination — and why corrosion and erosion damage to pipework and pumps is a repair-and-protect problem, not a replacement problem.
Read articleThe practical, UK-focused playbook for SME manufacturers adopting AI. Maturity model, highest-ROI use cases, build vs buy, common pitfalls, UK funding routes, and a 90-day adoption roadmap.
Read articleThe real year-1 cost of traditional BI for a UK SME manufacturer is £60,000–£100,000+, not the £1,700 sticker. The six hidden costs that derail SME analytics projects.
Read articleMost engineering analytics tools chase enterprise budgets. We chose the harder, more important market — SME manufacturers. The honest reasoning, the market structure, and why now is the right time.
Read articleThe argument that engineers should learn SQL misses the point. Engineers should solve engineering problems. Here's what no-code analytics really means — and how natural language is becoming the right interface.
Read articleVibration analysis is the most powerful tool in condition monitoring. How it works, what each fault signature looks like, what ISO 10816 means in practice, and how AI makes it accessible without specialist training.
Read articleChatGPT is impressive but produces confidently wrong answers when given real data. Hallucination, no persistent data access, and lost context explained — with the architectural fix that actually works.
Read articlePredictive maintenance used to be enterprise-only. In 2026, that's no longer true. What predictive maintenance actually is, what it really costs, where SMEs should start, and the mistakes that derail rollouts.
Read articleManufacturing analytics has changed more in the last 24 months than the previous decade. AI, accessible cloud platforms, and natural language interfaces are now within reach of every SME engineering team. The full picture, in one guide.
Read articleThe technology works. The benefits are documented. So why do most predictive maintenance proposals get stuck in finance review? Build the business case in finance language, with conservative numbers.
Read articleOverall Equipment Effectiveness is the gold-standard manufacturing metric — but most SMEs measure it imperfectly. AI surfaces hidden losses, automates loss categorisation, and makes OEE visible in real time.
Read articlePower BI is the default. But for SME engineering teams, the question isn't whether it can work — it's whether it works for them. Setup time, cost, ease of use, and engineering fit compared honestly.
Read articleMost engineering teams know reactive maintenance is expensive. Few have the tools to escape it. Here's how AI is changing that — not by replacing engineers, but by giving them visibility into failures weeks before they happen.
Read articleYour sensor data, maintenance records, and production logs are business-critical. Here's exactly how AWI Analytics protects them — JWT authentication, AES-256 encryption, organisation-scoped isolation, and more. No buzzwords, just the real security stack.
Read articleEquipment failures don't happen without warning — the signs are buried in your sensor data. How pattern-based anomaly detection surfaces the drift that precedes a failure, and what has to happen after the alert for it to be worth anything.
Read articleVibration, temperature, pressure, current — your sensors generate thousands of data points every day. Here's what each engineering data type actually reveals about equipment health, and how to turn raw readings into maintenance decisions.
Read articleTraditional BI platforms need specialist skills. Generic AI chatbots hallucinate. Retrieval-Augmented Generation (RAG) offers a third path — AI that stays grounded in your actual engineering data, with every answer traceable to source.
Read articleEngineering teams are drowning in data but starving for insight. AWI Analytics turns scattered operational data into clear, actionable answers in three simple steps. No coding. No dashboards. Just ask a question.
Read articleSiemens puts the cost of unplanned downtime at up to $260,000 per hour — and the hidden costs go far deeper. The real financial impact, and how condition data turns unplanned stops into planned work.
Read articleEngineering teams are caught between two imperfect options: AI chatbots that are easy but disconnected, and BI platforms that are powerful but need a dedicated analyst. Here's why neither is enough.
Read articleYour team is working hard and the data exists. But equipment still fails without warning, reports take hours to build, and nobody trusts the numbers. Here are 5 signs your tools are holding you back.
Read articleModern factories generate terabytes of sensor data every week. Yet most engineering teams still rely on spreadsheets, gut instinct, and reactive firefighting to make critical decisions.
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