Cameras, vibration probes, thermal arrays, acoustic microphones — paired with ML models running close to the signal. We benchmark which signals create operational value, then engineer integrated edge-perception systems that turn raw industrial physics into structured, real-time decisions.
In an industrial environment, the most valuable information is born at the asset — vibration on a pump, temperature on a motor, image streams from a camera over a conveyor, acoustic emissions from a press. Our sensor-intelligence practice combines hardware selection, physical instrumentation, edge compute, and machine-learning models into integrated systems that capture, interpret, and act on these signals at the speed they arrive.
We deploy where the data is generated. ML models run on industrial PCs, NVIDIA Jetson, embedded ARM, or PLC-adjacent gateways with bandwidth-efficient telemetry to centralized systems for trending and continuous learning. Latencies measured in milliseconds. Up-time measured in years. Maintenance protocols designed for plant electricians, not data scientists.
A working catalogue of the systems, models, and platforms our engineers ship within this practice — selected for industrial reliability, observability, and scale.
Defect detection, pose estimation, OCR/OCV, presence/absence verification, and dimensional measurement at line speed.
Bearing-fault detection, imbalance, misalignment, and remaining-useful-life models from accelerometer streams.
Hot-spot detection on electrical assets, motor temperature trending, process-condition monitoring via FLIR + ML.
Anomalous-sound detection on pumps, valves, and rotating machinery using contrastive deep-learning models.
Combine vision, vibration, current, and temperature signals into unified asset-health and process-state estimates.
Architect, deploy, and operate large-scale industrial-IoT fleets — from physical install to OTA model updates.
Representative deployments across our industrial client base. Each grounded in production engineering — not concept slides.
High-frame-rate cameras paired with edge-CNN models inspect every container for crack, leak, label, fill-level, and cap-orientation defects.
Continuous accelerometer monitoring on motors, fans, and pumps with signature analysis identifying bearing degradation weeks ahead of failure.
Pan-tilt thermal cameras over substations and switchyards with anomaly models flagging hot-spots, loose connections, insulation degradation in real time.
Computer-vision systems on plant CCTV verify hard-hat, glasses, and high-vis vest compliance and detect unauthorized entry into hazardous zones.
Microphone arrays capture press sounds; ML models distinguish normal from abnormal cycles, catching tooling wear and material faults invisible to other sensors.
Point-cloud capture at warehouse inbound docks for automatic carton dimensioning, pallet inspection, and damage detection.
Networked methane and VOC sensors with anomaly models triangulate leak sources and prioritize response.
Fusing vibration, current, temperature, and pressure signals into a single health score per compressor that feeds maintenance prioritization and leak-loss reduction.
Tools, frameworks, and platforms our engineers use day-to-day in this practice.
Every Pentaaxis engagement starts with a structured benchmarking conversation — no obligation, a senior engineer in the room, and a calibrated view of where AI moves the needle for your organization.