Not another dashboard. A diagnostic mind that reasons like your best engineer.
Most monitoring platforms collect data and draw lines on charts. AndonEAM is different. It starts from your RCM failure modes, selects the mathematically correct analytical model for each one, correlates live anomalies with past failure history, and tells you exactly what is degrading, why it matches a known failure pattern, and what to do about it — the way a senior reliability engineer would, except it never sleeps and it covers every asset simultaneously.
The Problem
Traditional condition monitoring collects sensor data, applies a fixed threshold or a generic ML model, and fires an alert. But here is the problem: no one told the algorithm what failures actually look like on this specific asset. It has no knowledge of your FMEA. It has never seen your failure history. It cannot distinguish a bearing degradation signature from a load transient. So it alerts on everything — and your best engineers waste their time triaging noise instead of preventing failures.
The industry calls it predictive maintenance, but if every alert requires a senior engineer to manually investigate, correlate, and diagnose — you have just moved the bottleneck, not eliminated it.
"Temperature high" is not a diagnosis. Your engineers need to know which failure mode is developing, which component is affected, and what action to take — not just that a number crossed a line.
Bearing wear, thermal degradation, cavitation, and seal leakage are physically different phenomena. A single generic ML model cannot reliably detect all of them.
Your RCM analysis identified 200 failure modes. Your monitoring platform knows about zero of them. All that engineering knowledge sits in a spreadsheet while the algorithm guesses.
When a pump fails, the insight dies with the work order. No traditional monitoring platform learns from your past failures to improve future detection.
How It Works
AndonEAM is engineered to remove every point of friction between raw engineering data and an executed, optimized maintenance programme.
AndonEAM automatically pulls the complete FMEA from your Automated RCM analysis — every failure mode, every component, every functional failure. This is the diagnostic knowledge base that grounds every alert in engineering reality, not statistical noise.
A senior reliability engineer does not use the same analytical approach for bearing wear, thermal degradation, and cavitation. Neither does AndonEAM. The AI evaluates each failure mode and autonomously selects the mathematically correct diagnostic model — from statistical fingerprinting, ARIMA forecasting, multivariate regression, unsupervised clustering, to supervised classification.
Your engineers map live telemetry streams — vibration, temperature, pressure, flow, current draw — directly to the failure modes and components they physically indicate. Every sensor signal now has an engineering purpose, not just a dashboard position.
Live sensor data is continuously analyzed through each failure mode's tuned diagnostic model. When an anomaly is detected, AndonEAM correlates it against the RCM failure mode taxonomy and past failure history to determine which specific degradation pattern is developing — and generates a diagnostic case with root cause hypothesis and recommended maintenance action.
Every diagnostic case lands in your reliability engineer's review queue with the full reasoning chain: which failure mode, which analytical model detected it, what the anomaly signature looks like, how it correlates with known failure patterns, and the recommended intervention — ready for approval or escalation.
Capabilities
Every monitoring rule is anchored to a specific failure mode from your FMEA. When an alert fires, your engineers know exactly which component is degrading, which failure mechanism is developing, and what the recommended maintenance action is — because the RCM analysis already defined it.
Static rules, statistical fingerprinting (Z-score/SPC), time-series ARIMA forecasting, multivariate regression, unsupervised clustering, and supervised classification — each autonomously assigned to the failure mode it was designed to detect.
Anomalies are not just flagged — they are correlated against your full failure history and RCM taxonomy. The system identifies which known degradation pattern the current data matches, providing a probable root cause before a human investigates.
Every diagnostic output includes the full reasoning chain: which failure mode, which model detected the anomaly, what the statistical evidence is, how it correlates with past failures, and the specific recommended maintenance action. Your engineers review a diagnosis — not a chart with a red line on it.
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