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.
Free 30-minute scoping call — we audit your existing telemetry before recommending new sensors.
Working systems you can open right now, without talking to us. There are no client logos below and no testimonials — we will not publish proof we cannot substantiate. What we can put in front of you is software hjLabs.in built, deployed and keeps running in public. Open it, push on it, and judge the engineering yourself before you spend time on a call.
hjLabs.in is a machine builder as well as an AI shop. The CNC router, selective soldering robot, wire cutter, spring coiler and IoT transformer winder below are our own products, on this same website, with real prices on them. The people writing the models also own the mechanics, the electronics and the firmware — very few AI consultancies can say that.
A Transformer deep-learning architecture for stock price prediction, published openly and used by other developers. The sequence-modelling work behind it is the same class of problem as forecasting from a long stream of timestamped sensor readings. Two related repositories are public alongside it: hjalgos_notebooks (110 stars) and PersonalGoalAssistant, a reinforcement-learning goal assistant (22 stars).
Turning a raw signal into a vector you can search and compare is the step every anomaly-detection pipeline is built on. This page does it live, in your browser, on your own GPU via WebGPU — no upload. It sits in a catalogue of 139 open models across 22 task types that we wired up and run ourselves.
We are a small specialist team, not a large consultancy, and we are not going to dress up as one. Here is what that actually buys you.
There is no account-manager layer between you and the person writing the code. The engineer who takes your scoping call is the engineer on your project, and stays reachable for the length of it.
hjLabs.in designs, manufactures and sells production machines — CNC routing, selective soldering, wire cutting, spring coiling, transformer winding. When a vision model has to survive a real factory line, cameras, lighting, PLCs and vibration are our day job, not a subcontractor’s.
Live tools you can open, open-source repositories you can read, and a standards implementation you can check line by line against the DICOM specification. You do not have to take a claim on trust when you can go and test it.
Four asset classes where the ROI math is consistently strong.
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. We also instrument our own shop-floor equipment — including the AutoSolder 6 soldering robot — so the same telemetry stack we sell to plants is the one we run in production ourselves.
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.
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.
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.
A six-stage pipeline from sensor to alert — every stage modular, every stage testable.
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.
MQTT/OPC UA into a hardened gateway, then into a time-series database (InfluxDB, TimescaleDB or AWS Timestream depending on scale and budget).
FFT bands, envelope spectrum, kurtosis, crest factor, statistical moments, autocorrelation features. Versioned in a feature store so retraining is reproducible.
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.
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.
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.
Battle-tested OSS plus selective managed services where they earn their cost.
IFM, Banner, Bosch CISS, MEMS triax, Advantech, Moxa, Raspberry Pi, Jetson
InfluxDB, TimescaleDB, AWS Timestream, MQTT (HiveMQ/EMQX), OPC UA
scikit-learn, PyTorch, TensorFlow, PyOD, anomalib, river, sktime, MLflow
Grafana, AWS SageMaker, Vertex AI, Azure ML, Kafka, n8n for alerting
Three engagement shapes, priced for the actual scope — not by sensor count.
Single asset class, 4-6 weeks
Plant-wide rollout, 12-16 weeks
Multi-site programme, 6-12 months
Six questions every plant or fleet manager asks before greenlighting a PdM project.
PdM works best alongside the right hardware, automation and AI add-ons.
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.
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