engineeringPredictive Maintenance · Enterprise-grade

Predictive Maintenance AI Services in India
Reduce Downtime 30–50%

Engineer retrofitting old factory lathe with vibration sensors for predictive maintenance

Predictive Maintenance (PdM) is the data-driven discipline that uses time-series machine learning, anomaly detection and Remaining Useful Life (RUL) models to forecast equipment failure before it occurs, lifting Mean Time Between Failures (MTBF) by 30–50%. At hjLabs.in we build end-to-end sensor-to-dashboard pipelines for manufacturers, utilities, fleets and facilities — from vibration, current and temperature ingestion to edge inference and CMMS integration. With 8+ years of industrial automation experience we typically catch bearing degradation 2–6 weeks ahead. Below: asset classes, build process, pricing from ₹4L, and FAQs.

calendar_todayBook Free Scoping CallfactorySee Case Studies
verifiedVibration + acoustic MLmemoryJetson edge inferenceintegration_instructionsSAP PM / Maximo / Ignition
68%
Avg Downtime Reduction
3.4×
MTBF Improvement
36+
Plant Deployments
8+ yrs
AI/ML Engineering

For high-volume sensor data, we layer agentic AI workflows on top of the ML pipeline — agents handle anomaly triage, root-cause analysis, and maintenance-order generation autonomously.

Where Predictive Maintenance Pays Off

Four asset classes where the ROI math is consistently strong.

precision_manufacturing

Manufacturing

Motors, gearboxes, conveyors, presses, CNC spindles, pumps and compressors. We typically catch bearing degradation 2 to 6 weeks before failure and gearbox issues 1 to 3 weeks ahead. A single avoided line stoppage on a high-throughput plant routinely pays for the entire pilot.

bolt

Transformers & Utilities

Distribution and power transformers, switchgear, capacitor banks. We monitor oil temperature, dissolved gas analysis trends, partial discharge, and load profiles. Our IoT transformer winding machine clients use the same telemetry stack their utility customers use for incoming inspection — a natural cross-sell.

local_shipping

Fleet & Logistics

Trucks, buses, last-mile EVs and yellow goods. We pull from existing OBD-II / FMS / J1939 telematics or add OBD dongles, then build per-vehicle models for engine, battery, brake and coolant systems. Roadside breakdowns drop and unscheduled garage time falls 25 to 40 percent.

ac_unit

HVAC & Building Systems

Chillers, AHUs, cooling towers, lifts and BMS-monitored equipment. We typically combine current-draw signatures, temperature deltas and runtime profiles to predict compressor and motor faults — particularly valuable for data centres, hospitals and large commercial real estate.

How We Build Predictive Maintenance

A six-stage pipeline from sensor to alert — every stage modular, every stage testable.

01 Sensor

Audit existing PLC/SCADA tags first. Add tri-axial vibration, current, temperature or acoustic sensors only where the existing signal is too coarse for the failure mode.

02 Telemetry

MQTT/OPC UA into a hardened gateway, then into a time-series database (InfluxDB, TimescaleDB or AWS Timestream depending on scale and budget).

03 Feature Engineering

FFT bands, envelope spectrum, kurtosis, crest factor, statistical moments, autocorrelation features. Versioned in a feature store so retraining is reproducible.

04 Anomaly + RUL Models

Two-stage approach: high-recall anomaly detector (IForest, ECOD, autoencoders) gates a per-asset RUL or fault-class classifier. We benchmark on your data before committing.

05 Alert Pipeline

Per-asset thresholds, hysteresis windows, dedup, severity scoring. Alerts open work orders in your CMMS (SAP PM, Maximo, eMaint, UpKeep, Fiix) with sensor traces attached.

06 Dashboard

Grafana for engineers, a custom web app for plant managers. Includes a feedback loop where operators mark alerts true/false — that label drives the weekly retrain.

Predictive maintenance dashboard with vibration FFT analysis and anomaly alerts

Predictive Maintenance Tech Stack

Battle-tested OSS plus selective managed services where they earn their cost.

Sensors & Gateways

IFM, Banner, Bosch CISS, MEMS triax, Advantech, Moxa, Raspberry Pi, Jetson

Time-Series & Streaming

InfluxDB, TimescaleDB, AWS Timestream, MQTT (HiveMQ/EMQX), OPC UA

ML & Anomaly Libraries

scikit-learn, PyTorch, TensorFlow, PyOD, anomalib, river, sktime, MLflow

Cloud & Visualisation

Grafana, AWS SageMaker, Vertex AI, Azure ML, Kafka, n8n for alerting

Engagement Tiers

Three engagement shapes, priced for the actual scope — not by sensor count.

Audit + Pilot

$4,000–$9,000

Single asset class, 4-6 weeks

  • Telemetry & SCADA audit
  • Sensor recommendation
  • One asset class modelled
  • Grafana dashboard demo
  • 30-day support
Start Pilot

Production

⭐ Most Popular
$25,000–$60,000

Plant-wide rollout, 12-16 weeks

  • 3-6 asset classes covered
  • CMMS integration (SAP/Maximo/etc)
  • Edge inference where needed
  • Drift & alert dashboards
  • Weekly retrain pipeline
  • 6 months support
Talk to Us

Enterprise

$80,000–$200,000+

Multi-site programme, 6-12 months

  • Multi-site / multi-plant
  • Centralised model registry
  • RBAC, audit, ISO 27001-ready
  • Active-learning loop
  • Dedicated SRE / on-call
  • 12 months support
Get Quote

Frequently Asked Questions

Six questions every plant or fleet manager asks before greenlighting a PdM project.

Both work and we usually combine them. About 70 percent of engagements start with what is already there: PLC tags, SCADA historian, motor current, temperature, flow meters, CMMS work-order history. That alone is enough for many asset classes. Where the existing signal is too coarse — for example, a vibration RMS scalar but the failure mode requires the full FFT spectrum — we add tri-axial vibration sensors and an LTE/Wi-Fi gateway. Typical retrofit per critical asset: USD 600 to 1,800 in hardware.

Yes. We have integrated PdM pipelines with Siemens WinCC, Rockwell FactoryTalk, AVEVA Wonderware, GE Proficy, Ignition and home-grown SCADA via OPC UA, MQTT, Modbus TCP and historian SQL. On the CMMS side we have written work-order back-pushers for SAP PM, IBM Maximo, eMaint, UpKeep, Fiix and Tally-based home-grown systems. Sensor/SCADA flows into our time-series DB, ML scores it, alerts open a CMMS work order with predicted failure mode and a sensor-trace attachment. Maintenance teams keep their existing tools.

Pilots break even in 6 to 14 months, plant-wide rollouts in 9 to 18 months, multi-site programmes in 12 to 24 months. ROI on a typical pilot — one critical asset class, USD 6,000 to 9,000 build cost — is dominated by avoided downtime cost. If a single bearing failure costs USD 25,000 to 60,000 in lost output and one to three are caught per year, payback is fast. ROI is much weaker on cheap, redundant equipment — sometimes a USD 50 vibration alarm relay is the right answer, and we will say so.

Two-stage classifier: stage one is high-recall anomaly detection (PyOD, IForest, ECOD), stage two is a per-asset RUL or fault-class model that fires only when stage one has been hot for N consecutive windows. Every alert carries a confidence, a sensor trace and the top three suspected causes. Operators mark alerts true/false in one click; that label drives a weekly retrain. We tune per-asset thresholds rather than running one global threshold — alone this typically cuts false-positive rate 60 to 80 percent.

Yes, and for high-frequency vibration data this is usually the right architecture. We deploy lightweight anomaly models on Raspberry Pi 5, Jetson Nano, Jetson Orin Nano or industrial gateways like Advantech and Moxa. The edge node computes features (FFT bands, kurtosis, crest factor, envelope spectrum) at full sample rate and ships only features plus alerts upstream. Raw waveforms stay locally for 7 to 30 days for forensic playback. For sites with no internet, we run a fully offline edge stack with on-device dashboards.

Yes. About a third of our PdM engagements are advisory: architecture review, sensor and time-series DB selection, baseline model recommendations, MLflow/feature-store setup, and a written playbook your team executes. We hand over reusable code (ingestion, feature pipelines, eval harness) under a clean licence so you can extend it without lock-in. Where your team is strong on ML but new to industrial signals, we pair for the first asset class — your engineers ship the second alone, and we move to a quarterly review cadence. USD 800 per day for advisory.
Old factory lathe machine with vibration sensors mounted for predictive maintenance retrofit

Related Services & Products

PdM works best alongside the right hardware, automation and AI add-ons.

All AI/ML Services →

Agentic AI, RAG, MLOps and more.

AI for Manufacturing →

PdM, defect detection, yield optimisation.

SensorSync IoT Platform →

Our turnkey industrial IoT telemetry stack.

IoT Transformer Winding Machine →

Field-proven IoT integration on real industrial hardware.

Automation Components →

PLCs, drives, sensors and gateways we build with.

Ready to Cut Unplanned Downtime?

Bring three months of historian or telemetry data. We will tell you in 30 minutes whether predictive maintenance is the right tool for your assets.

calendar_todayBook a Free Scoping Call
Premium engineering · Fixed-scope pricing

Cut unplanned downtime 30-68% in 9 months

We instrument your bearings, motors, and gearboxes; train on plant-floor data (not synthetic CWRU); and integrate alerts straight into your SAP PM or Maximo. Free 60-min call sizes the pilot honestly.

calendar_todayBook Free 60-min Call chatWhatsApp +91 7016525813
verified_userOn-prem & air-gappedscheduleReply within 24hpaymentsFixed-scope pricing
WhatsApp Us