Building Data-Centric Edge AI for Motor Health & Predictive Maintenance
Online Training Workshop: Build, Learn, Deploy
Overview
This workshop is designed to introduce developers to the hardware and software required to build data-centric Edge AI applications for motor control health monitoring and predictive maintenance. Participants learn how motor-related data, such as current, vibration, temperature, and speed, is acquired, processed, and managed deterministically at the device, and how this data is transformed into AI-ready features for on-device inference. The workshop covers the selection and integration of edge hardware (MCUs, MPUs, NPUs, sensors, and accelerators) alongside production-grade software for real-time control, time-series data management, signal processing, and Edge AI frameworks. Through hands-on exercises, developers gain a clear understanding of how data management, analytics, and AI work together to detect anomalies, predict failures, and enable reliable, explainable motor health intelligence directly at the edge.
Workshop Date: Thursday, March 19, 2026
Workshop Time: 11:00 AM–2:00 PM PT (2:00 PM–5:00PM ET)
Workshop Location: Online
Online Tutorial Workshop Syllabus
Objective: Detect abnormal motor behavior in real-time on an Edge device. Build data-driven device firmware to ingest raw signals, produce cleaned features, score motor health with Edge AI, and visualize a health timeline.
The workshop comprises three 60-minute modules.
Module 1: Data-Driven Firmware Design and Programming
- Sensors to sample motor phase current and vibration
- Hardware setup: embedded device, storage media, and communication channels
- Data modeling for edge devices: motor health & predictive maintenance
- Ingest/store samples, features, inference, events, and more
- Sampling rates, stream window sizes, and key columns
- Data cleaning and conditioning with deterministic stream buffering
- Per-channel filtering, normalization, outlier detection, and dropout handling
- Transactional commits and time series storage
- Feature engineering for Edge AI: RMS, peak, kurtosis, mean, variance
Module 2: Building an Edge AI Pipeline
- Select AI model algorithm: anomaly detection vs. classification
- Signal data set with faults/disturbances: mechanical imbalance, load changes, misalignment, bearing roughness
- Train a model with an Edge AI development tool
- Training with healthy-only data, validate with healthy+disturbances
- Assess model size, latency, determinism
- Export model to embedded runtime
- Real-time inference and health logic pipeline
- Integrate ML model inference into feature streams to capture anomaly score per window
- Set thresholds, develop persistence logic, and create health state machine
- Inject imbalance and observe anomaly score transitions
Module 3: Edge Device Analytics, Visualization, and Connectivity
- Communicate with ITTIA Data Connect over Ethernet, WiFi, and/or serial UART
- Create a custom on-device summary dashboard with ITTIA Analitica
- Average motor current, peak vibration noise, number of anomalies, health score
- Show data in web dashboard UI, web services (HTTP/REST), Python, C/C++
- Analyze anomaly frequency, duration of abnormal behavior, severity
- Correlate vibration patterns with current signatures
- Score motor health: anomaly count, severity trends, noise and vibration peaks
- Use anomaly data for diagnostics and root cause analysis
- Validate with instrumentation: bounded latency per window, minimal storage wear
Workshop Sample Application
During this hands-on workshop, you will be trained to design, build, and deploy a real-world, data-driven AI application, gaining practical skills you can immediately apply to embedded and edge systems.
Anomaly Detection in an Electric Motor
During the workshop, participants step into the role of an embedded engineer responsible for keeping an electric motor running reliably in a demanding real-world environment. Using MCU devices as the hardware foundation, they begin by capturing live motor current signals—no extra sensors, no cloud dependency, just raw electrical behavior at the edge. The team trains an on-device AI model that learns what "normal" looks like for the motor and continuously adapts as operating conditions change.
As the motor runs, subtle abnormalities begin to appear, slight variations in current, brief bursts of noise, early warning signs invisible to traditional threshold-based logic. The AI detects these anomalies in real time, while ITTIA DB Lite persistently stores and organizes the results on the device itself. Together, AI and data management transform raw signals into insight: anomaly frequency, duration, and severity are analyzed to compute a live motor health score, alongside on-device statistics such as average current, peak noise levels, and anomaly counts. By the end of the session, participants have built a complete predictive maintenance solution, one that demonstrates how AI plus deterministic embedded data management enables smarter, safer, and more reliable industrial systems directly at the edge.
Course Details
Workshop Cost: USD $250 per attendee
The workshop fee includes:
- Complimentary 3-month license of the ITTIA DB Platform software
- Comprehensive lecture materials
Workstation: This is a hands-on workshop. You must have an STM32H573I-DK device, a USB-C cable, and a Windows PC:
*Unlock 25% OFF your STM32H573I order when you register!
Edge Data AI Certification for Embedded Devices
Workshop attendees will receive ITTIA DB Platform certification. This certification validates hands-on expertise in using the ITTIA DB Platform for deterministic data management, on-device data processing, and Edge AI–ready data handling. It demonstrates and validates the ability to design reliable, power-fail-safe, and AI-enabled embedded systems using proven data architectures, reducing development risk, accelerating time to production, and enabling high-quality, scalable solutions.
Past Workshop Participants
ITTIA has delivered highly sought-after, hands-on workshops that attract engineers and innovation leaders from across multiple sectors. Our sessions are trusted by companies building real-world, data- and AI-driven embedded systems, and have been attended by teams from leading organizations committed to advancing intelligent edge technologies, including:
Abbott – Acuity Brands – Advances Energy – Applied materials – Avery Biomedical Devices, Inc. – Badger Meter – Cardios – Cat Wranglers
Cognosos Inc. – Continental AG – CPI – Declarative Futures – EDS – Emtech – Everspin – EXFO – Honeywell – Kellogg Northwestern
L3Harris – Magna International – Mayo clinic – Megavolt Labs – Milwaukee Tool – Mold Masters – Nidec Global Appliances – OTTO Engineering
Philips – Resideo – Schneider Electric – Siemens – Southland Sensing Ltd. – TITOMA – TTI Floor Care – Wind River – Zetron/Codan
Audience
These immersive, hands-on workshops are built for embedded architects and engineers who are shaping the next generation of data-driven, AI-enabled edge systems and want more than theory, they want results. Through practical, expert-led sessions, participants learn how to select the right device platform, design deterministic real-time firmware, and implement robust on-device data management that elevates data to a first-class system asset. The program goes further by showing how to prepare and integrate optimized AI pipelines directly into embedded workflows, while addressing cybersecurity, functional safety, diagnostics, and long-term lifecycle management from day one. By the end of the workshop, engineers leave with the confidence, skills, and real-world experience needed to build intelligent, trustworthy, and future-proof embedded systems ready for production.
These workshops are highly technical, intended for professional engineers and are not suitable for the general public without relevant qualifications.
