AI System for Industrial IoT: Monitoring and Predictive Analytics

Unplanned downtime of a centrifugal compressor at a chemical plant costs $20k per hour. Vibration sensors rise, but SCADA cannot distinguish normal wear from pre-failure state. An AI model based on LSTM-Autoencoder captures the difference a month before failure. We deploy such solutions: collecting data from sensors (vibration, current, temperature), training models, integrating into the control loop — without stopping production. Typical savings on maintenance are 30-50% of budget, payback in less than 12 months.

Problems Solved by AI in IIoT

Disparate Data

Modern machines use OPC-UA, old PLCs use Modbus, analog sensors use 4-20 mA. Aggregating everything into a single bus without time loss is nontrivial. We use OPC-UA servers and IoT gateways for unification.

Delayed Diagnostics

SCADA and Historian (OSIsoft PI, Aveva PI) store history but do not uncover hidden patterns. LSTM-Autoencoder analyzes multivariate time series in real time.

Noise and False Alarms

Classical threshold rules yield 50% false positives. Operators get used to ignoring alerts. AI reduces false alarms to 2%.

How AI Analytics Prevents Emergency Stops

AI continuously analyzes multivariate time series and identifies anomalies invisible to humans. LSTM-Autoencoder detects micro-shifts in vibration-temperature correlation several days before failure, allowing scheduled replacement. In our chemical plant case, emergency stops dropped by 70%.

IIoT AI Platform Architecture

Data Acquisition Layer: OPC-UA servers for modern equipment, Modbus TCP/RTU for legacy PLCs, 4-20 mA converters with IoT gateways. Historian as historical data source.

Edge Processing: Industrial PCs (Siemens IPC, Advantech) or rugged Jetson Nano. MQTT Sparkplug B for standardization. Local ML inference for latency-critical tasks.

AI Analytics:

• Predictive Maintenance: anomalies in vibration, current, temperature → RUL prediction. LSTM-Autoencoder, Isolation Forest. Accuracy MAE <10%.
• Process Optimization: RL or Bayesian Optimization for parameter tuning. Energy savings up to 15%.
• Quality Prediction: online product quality prediction, defect reduction by 20%.

Why Edge Processing is Critical for IIoT

On Edge, AI performs inference in 100-300 ms without cloud latency, enabling equipment shutdown before critical events. Other data is sent to cloud for retraining.

How We Do It — Predictive Maintenance Compressor Case Study

At a nitrogen fertilizer plant, we deployed a system on 12 centrifugal compressors. Sources: vibration (ICP accelerometers, 10 kHz), stator current, bearing temperature, oil pressure. Collection via Modbus TCP and 4-20 mA NI modules.

Edge layer: Siemens IPC427E with Ubuntu + TensorFlow Lite. LSTM-Autoencoder model trained on 6 months of normal operation. First failure predicted 12 days before vibration exceeded threshold. Reaction time 300 ms on Edge. As noted in the LSTM-Autoencoder work, detection accuracy reaches 95%.

Technical details of LSTM-Autoencoder architecture: encoder and decoder with two LSTM layers of 64 neurons, dropout 0.2, Adam optimizer. Model trained on normal data; anomalies detected via reconstruction error (MSE). Threshold chosen by percentile on validation.

Process: From Audit to Deployment

1. Data source audit — sensor inventory, protocols, controllers.
2. Architecture design — select Edge devices, data bus (MQTT Sparkplug B), vector DB (InfluxDB or TimescaleDB).
3. Model development — train on historical data (LSTM, XGBoost, Isolation Forest).
4. Integration and testing — connect to MES/ERP via REST API, pilot on 1-2 units.
5. Industrial launch — scale, calibrate thresholds, train operators.

What Is Included in the Work

• Architectural documentation and data model.
• Trained and deployed AI models (Docker containerization).
• Alert and dashboard configuration (Grafana + InfluxDB).
• Integration with MES/ERP.
• Training for technologists and maintenance staff.
• 3-month warranty support after launch.

Timelines and Cost

Implementation takes 12 to 24 weeks. Cost is calculated individually after audit. Order a free audit of your production to get a preliminary estimate. Contact us for a consultation.

Our experience: over 5 years in industrial analytics, 15+ IIoT projects for chemical, oil & gas, and machine building. We use proven models with MAE <10% on benchmark datasets.

Cybersecurity for IIoT

OT/IT convergence introduces new attack surface. We implement network segmentation, anomaly detection at network layer (Claroty, Nozomi Networks), data encryption on Edge. Compliance with IEC 62443.

Which Industrial Protocols Does the AI Platform Support?

Edge AI and Optimization: How to Deploy Models Without Cloud?

Imagine: your face recognition model has 4 seconds latency on Jetson Orin, the battery runs out in an hour, and the model crashes with OOM. We are a team of Edge AI engineers with 5+ years in production — we have optimized over 150 models for edge devices. Without profiling and proper choice of quantization or distillation, the project is doomed. The gap between research code and edge deployment is a separate engineering discipline; we help you master it in 2–16 weeks turnkey. Edge AI and model optimization services are not just export, but systematic work with hardware.

Why Simply Exporting a Model Doesn't Work?

A PyTorch model with float32 and batch_size=32 is not ready for edge. Typical problems:

• ResNet-50 in fp32 occupies 98 MB, inference on Cortex-A78 — 380 ms. After INT8 quantization via torch.ao.quantization — 24 MB, 95 ms. Export to ONNX + TensorRT on Jetson — 28 ms.
• YOLOv8m on Raspberry Pi 5 in fp32 — 2.8 fps. TFLite INT8 — 9.4 fps. With XNNPACK delegate — 14 fps (1.5× faster than pure INT8).
• Transformer encoder on mobile CPU: MobileBERT in fp16 via CoreML on iPhone 15 — 18 ms/inference. distilbert-base-uncased in ONNX — 42 ms.

The problem is not choosing "quantize or not" — the right path is determined by the device, task, and acceptable metric degradation. We offer an assessment of your project: within 24 hours we will tell you how feasible it is to speed up the model.

How to Choose Quantization Method for Your Task?

PTQ (Post-Training Quantization) — a quick path. Take a trained model, run a calibration dataset (200–1000 samples), get INT8 or INT4 weights. Tools: torch.ao.quantization, ONNX Runtime quantization tool, bitsandbytes. Accuracy degradation: 0.5–2% on classification. Red zone — small object detection and segmentation, where PTQ gives -4–8% mAP.

QAT (Quantization-Aware Training) — training with simulated quantization noise. More expensive (retraining), but degradation 0.1–0.5%. Justified when PTQ is unacceptable. In PyTorch — torch.ao.quantization.prepare_qat().

GPTQ / AWQ — for LLMs. AWQ better preserves quality at 4-bit quantization. llm-compressor from Neural Magic or autoawq are the main libraries.

Potential savings from choosing the right method can be substantial — for example, reducing cloud inference costs by up to 70% when deploying to edge. Project cost is calculated individually based on model complexity and target platform.

When to Use Pruning vs Distillation?

Structural pruning removes channels or layers. torch.nn.utils.prune — basic tool. For transformers — attention head pruning (LTP, movement pruning). Result: ResNet-50 after removing 40% of channels with fine-tuning — -35% size, -28% latency, -1.2% top-1 accuracy.

Knowledge distillation — train a small student to mimic a large teacher. Classic via KLDivLoss on soft labels. Feature distillation on intermediate layers is more effective. Hugging Face DistilBERT: 66M vs 110M parameters, -40% latency, -3% on GLUE. This is a model compression technique.

Combined approach: distillation → pruning → QAT. Gives maximum effect on limited hardware. We recorded a case where a client achieved 70% reduction in cloud compute spend after moving to edge with this pipeline.

Target Platforms and Tools

TensorRT — the main tool for NVIDIA edge. TRT builds a graph with operator fusion, selects optimal kernels. On Jetson AGX Orin YOLOv8m in TRT INT8 gives 78 fps vs 22 fps in fp16 PyTorch — 3.5× improvement.

Practical Case: How We Detected Defects on a Production Line (Our Client)

Task: real-time scratch detection on metal, 30 fps, camera to Jetson Xavier NX (16GB). Original model YOLOv8l mAP50 0.91, server inference 28 ms, on Jetson in fp16 — 110 ms (9 fps). Not suitable.

Optimization steps we performed for our client:

1. Switch to YOLOv8m — mAP50 0.887 (-2.3%), 68 ms
2. Export to TensorRT FP16 via yolo export format=engine half=True — 31 ms (32 fps)
3. INT8 calibration on 500 frames — 22 ms (45 fps), mAP50 0.879

Result: 3.5% degradation at 5× speedup. Client received engine and documentation. We guarantee metric will not drop below agreed threshold — specified in contract.

What is Included in the Work?

• Report on model profiling on target device (layer latency, bottlenecks)
• Selection and justification of optimization methods (quantization / pruning / distillation)
• Optimized model (TensorRT engine / TFLite / CoreML / ONNX)
• Configs for reproducibility (scripts, Docker image, instructions)
• Testing on real device (at least 10,000 inferences)
• Training of your team (2 hours online)
• 1 month support after delivery

How to Order Model Optimization

1. Submit a request on the website or contact us in any convenient way.
2. We perform free profiling of your model on the target device within 24 hours.
3. We prepare an optimization plan with trade-off estimates (speed vs quality).
4. You approve the plan — we start work.
5. After completion, we deliver the optimized model, configs, and documentation.
6. We train your team and provide monthly support.

Timeline: optimization of an existing model — 2–4 weeks. Development from scratch for edge — 6–16 weeks.

Get a consultation — we will evaluate your model for free and offer a plan within 24 hours. Order free profiling now. For complex projects, contact our engineering team to discuss custom optimisation strategies.