Predictive Maintenance
AI Predictive Maintenance for Manufacturing - From Failure Signal to Resolved Work Order
Predictive maintenance without agentic AI is just a better alarm. It tells you the bearing will fail in 22 days - and then waits for a human to email three people, check parts stock in a spreadsheet, and argue about the maintenance window. That coordination chain takes two weeks. The bearing fails in three days.
Ajinkya Technologies builds predictive maintenance systems that close the entire loop - from IIoT sensor signal to scheduled, resourced work order in the MES - without manual routing. The prediction is just the input. The agent is what makes it operationally real.
The IIoT data backbone - connecting every machine to the prediction engine
Predictive AI is only as good as the signals feeding it. We start by connecting your machines - PLCs, drives, pumps, compressors, conveyors, and legacy equipment with no native connectivity - using OPC-UA, Modbus RTU/TCP, and MQTT into a Unified Namespace. Every asset gets a single, contextual address on the data layer.
Edge devices sit at the machine level and capture vibration (three-axis accelerometer), temperature, current draw, pressure, and cycle signatures at high frequency - typically 1,000 to 10,000 samples per second for rotating equipment. That raw telemetry streams to a time-series historian, which feeds both the ML models and the real-time dashboards your maintenance supervisor actually uses.
For legacy machines with no PLC - blast furnace auxiliaries, older rolling mill drives, utility compressors - we retrofit non-invasive sensor clamps and edge gateways. No modification to the existing control system. No production disruption during installation.
- OPC-UA, Modbus RTU/TCP, and MQTT for new and legacy equipment
- Three-axis vibration, temperature, current, pressure, and cycle-count capture
- Edge-to-historian pipeline with local buffering - line keeps running if cloud connectivity drops
- Unified Namespace: every machine signal has one canonical address for AI consumption
- Legacy machine retrofit via non-invasive sensor clamps, no PLC modification required
ML failure models - from sensor patterns to failure forecasts
Once the data layer is live, our ML engineers train failure models specific to your equipment classes and your operating conditions. A blast furnace fan bearing in Vijayanagar behaves differently from an automotive stamping press in Pune - generic models miss the patterns that matter in your plant.
We use a combination of anomaly detection (isolating deviations from the machine's own historical baseline), degradation curve fitting (tracking how fast a component is deteriorating and projecting its failure date), and multi-variate correlation (finding which combination of temperature + vibration + current predicts imminent failure for each asset class).
The output is not a threshold alarm. It is a failure probability score per asset, refreshed continuously, with a projected failure window - "Motor M-07 on Line 3 has an 87% probability of failure within 8-14 days based on bearing frequency signature degradation." That is what the agentic layer acts on.
- Asset-specific ML models trained on your equipment's own historical baseline
- Anomaly detection, degradation curve fitting, and multi-variate correlation
- Failure probability scores with projected failure windows, not threshold alarms
- Continuous model retraining as new failure events are confirmed
- Supports rotating equipment, hydraulics, conveyors, compressors, HVAC, drives
Agentic work order creation - the prediction becomes action
A prediction with no action is a dashboard metric. When our predictive system identifies an imminent failure, an AI agent picks up the signal and executes the coordination chain that normally takes two weeks of email: checks the maintenance schedule for the nearest available window that does not conflict with production targets, queries the ERP or spare parts store for the required components, checks technician availability and skill certification for that asset class, drafts a maintenance work order in the MES with the failure context, recommended action, parts list, and scheduled window attached, and notifies the maintenance manager for one-click approval - or executes autonomously under pre-approved guardrails for low-severity actions.
The entire sequence - prediction to draft work order ready for approval - takes under 60 seconds. The maintenance manager sees a work order, not an inbox full of forwarded alerts.
Every agent action is logged with full audit trail for ISO 27001 and SOC 2 compliance. Human-in-the-loop checkpoints are configurable per asset criticality.
- Automated schedule conflict check against production plan before booking maintenance window
- ERP/spare parts query - confirms parts availability before the work order is raised
- Technician availability and skill certification check
- MES work order creation with failure context, parts list, and window pre-populated
- Manager notification with one-click approval or fully autonomous execution under guardrails
- Full audit log of every agent action - ISO 27001 and SOC 2 compliant
Industries and equipment classes we cover
We have deployed predictive maintenance across some of the most demanding industrial environments in India and internationally. Our references include blast furnaces, coke ovens, rolling mills, and processing lines at JSW Steel's Vijayanagar complex - $9.6B+ of critical machinery under continuous monitoring and VSOP-based maintenance management.
Beyond steel, we deploy for automotive assembly (stamping, welding, painting line equipment), pharmaceutical manufacturing (filling lines, HVAC, utilities - FDA 21 CFR Part 11 compliant), FMCG (packaging lines, compressors, conveyors), paint and chemicals (reactors, agitators, pumps), and industrial equipment manufacturers.
- Steel & metals: blast furnaces, coke ovens, rolling mills, cranes, utility equipment
- Automotive & EV: stamping, welding, painting, assembly, test bench equipment
- Pharmaceutical: filling lines, HVAC, clean utility systems (FDA 21 CFR Part 11)
- FMCG: packaging lines, conveyors, compressors, refrigeration
- Paint & chemicals: agitators, pumps, reactors, heat exchangers
- Industrial equipment: any rotating or reciprocating equipment class
Frequently asked questions
What is AI predictive maintenance in manufacturing?
AI predictive maintenance uses machine learning models trained on IIoT sensor data - vibration, temperature, current draw, and cycle signatures - to forecast equipment failures before they cause unplanned downtime. Unlike condition monitoring that alerts when a threshold is crossed, predictive AI identifies the degradation pattern and projects a failure window days or weeks in advance. When combined with agentic AI, the system automatically drafts a repair plan, checks parts inventory, and raises a work order in the MES - compressing the manual coordination chain from days into seconds.
How much downtime reduction does predictive maintenance deliver?
Mature adopters report 30-50% reduction in unplanned downtime and 5-10% OEE improvement from production-grade predictive maintenance programs. At JSW Steel's Vijayanagar complex, Ajinkya Technologies manages over $9.6 billion of critical machinery including blast furnaces, coke ovens, rolling mills, and processing lines using VSOP-based maintenance systems with real-time monitoring.
Can predictive maintenance work on legacy machines without a PLC?
Yes. We retrofit legacy machines with non-invasive edge sensors - vibration clamps, current transformers, temperature probes - and connect them via Modbus or MQTT without modifying the existing control system. The edge device processes signals locally so the line keeps running even if the cloud connection drops. This means you can start predictive maintenance on your oldest, most critical equipment without any PLC upgrade or production stoppage.
What IIoT protocols do you use for predictive maintenance?
We connect machines using OPC-UA (the industrial standard for modern PLCs and SCADA), Modbus RTU/TCP (for legacy drives and controllers), and MQTT (for lightweight, high-frequency sensor telemetry from edge devices). All signals are normalised into a Unified Namespace so every asset has a single contextual address that feeds the ML models and AI agents cleanly.
What is the difference between predictive maintenance and agentic AI in manufacturing?
Predictive maintenance forecasts when a machine will fail. Agentic AI acts on that forecast - checking the maintenance schedule, querying the spare parts store, verifying technician availability, and raising a work order in the MES, all without human routing. The prediction is the input; the agent is what makes the prediction operationally useful. Without agentic automation, even a perfect prediction still requires two weeks of manual coordination before a wrench turns.
How long does a predictive maintenance deployment take?
We start with a focused proof-of-value on your most critical asset class - typically 4-8 weeks from sensor installation to live failure predictions on one line or cell. That first deployment proves the ROI case and defines the sensor strategy for plant-wide rollout. Full plant coverage scales from there in phases, with each phase live and delivering value before the next begins.
Explore related solutions
Talk to our manufacturing engineering team
Tell us your most critical asset, your current maintenance approach, and your biggest downtime problem. We will scope a predictive maintenance pilot - sensor selection, connectivity architecture, failure model approach, and MES integration - with a clear ROI target before any commitment.
Book a consultation