Bruviti
Use case

Remaining Useful Life Prediction

Predict component-level RUL for heaters, pumps, and bearings with ≤10% MAPE on critical assets; deliver ≥80% early warnings ≥7 days ahead; auto-create ≥70% proactive work orders with parts preallocated and policy-compliant updates across systems.

Challenge

High-value tools rely on calendar-based maintenance that ignores real operating stress. Teams face premature replacements or surprise failures. Telemetry lives in historians and PLCs, while maintenance and parts data sit in CMMS and ERP, creating silos. Analysts spend hours extracting signals without consistent, component-level forecasts. Unplanned downtime drives rush parts, overtime, and missed production targets.

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The objective

predict component-level RUL for heaters, pumps, and bearings with ≤10% MAPE on critical assets; deliver ≥80% early warnings ≥7 days ahead; auto-create ≥70% proactive work orders with parts preallocated and policy-compliant updates across systems.

Solution: How the AI layer changed the operating model

Learning and setup

Powered by Bruviti’s AI Operating Layer, the agent solution applied its predictive, forecasting, and policy models including LSTM and temporal CNNs, survival analysis, gradient boosting, and anomaly detection governed by a policy engine. Training data came from IoT and SCADA historians, PLC telemetry, duty-cycle metrics, process setpoints, ambient sensors, CMMS and EAM work orders with failure labels, parts replacement history, supplier life curves, FMEA notes, and the equipment ontology. This enabled the AI agent to recognize and interpret asset and component IDs, serial and model, timestamps, sensor tags like temperature, vibration, current, pressure, flow, run-hours, duty cycle, alarms and fault codes, maintenance events, part numbers, warranty terms, and location context.

RUL Training Data and Models workflow diagram DATA SOURCES MODEL TRAINING Historians PLC telemetry CMMS EAM ERP inventory Supplier life curves FMEA notes Equipment ontology Feature engine Training engine RUL models

Workflow orchestration

The AI agent reads streaming telemetry, fuses context from Equipment, Asset Management, Ticket Workflow, and Inventory modules, and routes actions. It computes health indices, estimates RUL, and applies policy rules to determine next steps such as open work order, reserve parts, propose a maintenance window, or notify teams. Branching covers lead time risk, warranty coverage, and SLA priority to ensure compliance at every step.

RUL Agent Solution Architecture diagram RUL agent Stream ingest Ontology graph Model service Policy engine Ticket module Inventory module ERP CMMS Notificationservice

Execution and resolution

The AI agent cleans and featurizes signals, runs online inference to compute health indices and RUL, calibrates thresholds by component class and SLA, then executes: create work order, reserve parts for just in time replacement, update calendars, and send targeted alerts. Responses complete in seconds, with updates posted across connected systems. Exceptions like sensor drift, missing tag mappings, or low confidence cases route to engineering with full context attached.

RUL Workflow Execution diagram Sensor stream Health index RUL estimate Policy check Creatework order Reserve parts Schedule window Notify team Route toengineer Update records

Results

30-50%Reduction in unplanned downtime
≥80%Early failure alerts with ≥7 days lead time
40%Fewer rush parts orders

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Frequently asked questions

What is remaining useful life prediction and how does it work for industrial equipment?

Remaining useful life prediction uses machine learning models trained on sensor telemetry, maintenance history, and degradation curves to estimate when a specific component will fail. Models like LSTM networks and survival analysis process real-time vibration, temperature, and current data to compute health indices and forecast failure windows days or weeks ahead.

How does predictive maintenance scheduling use remaining useful life estimates?

RUL estimates feed directly into maintenance scheduling by identifying optimal intervention windows. When a component health index drops below a calibrated threshold, the system auto-creates work orders, reserves spare parts, and proposes maintenance windows aligned with production schedules. This eliminates calendar-based replacement and reduces rush parts orders by up to 40 percent.

What accuracy levels can AI achieve for component-level life prediction?

Well-trained models achieve mean absolute percentage error of 10 percent or less on critical components like pumps, heaters, and bearings. Accuracy depends on sensor coverage, historical failure labels, and operating condition diversity in training data. Continuous online inference with automatic model retraining maintains prediction quality as equipment ages and operating patterns shift.