TSS AFCI MCXN547 Project

Introduction

This document provides guidance for configuring and running the TSS AFCI embedded application on the MCXN547 platform. The project supports arc fault detection, multi-channel data acquisition, and flexible deployment modes.

AFCI Board

System Overview

The application is a configurable embedded solution designed for:

  • Real-time arc fault detection (AFCI)

  • High-speed data acquisition

  • Multi-channel signal processing

It supports multiple operating modes and allows flexible integration with machine learning models generated by Time Series Studio (TSS).

Development Environment Setup

IDE Installation

Download the official MCUXpresso IDE from the NXP website: https://www.nxp.com/design/software/development-software/mcuxpresso-software-and-tools-/mcuxpresso-integrated-development-environment-ide:MCUXpresso-IDE?tab=Design_Tools_Tab

Project Import

  • Open MCUXpresso IDE.

  • Go to Quickstart Panel → Import project(s) from file system.

  • Select the provided project package (.zip).

SDK Installation

  • Download the MCXN547 SDK from the MCUXpresso SDK Builder.

  • Install the SDK into the IDE before building the project.

Application Configuration

Operation Modes

The application supports three execution modes controlled by the SW1 setting:

Mode

Description

0 or 3

Prediction Mode – Executes real-time inference on input data streams.

1

Data Logging Mode – Captures raw data for offline analysis and model training.

2

Data Logging Mode with Auto-Labeling – Captures data with automated label generation for supervised learning.

Channel Configuration

The number of active ADC channels is controlled via the DEVICE_QUANTITY macro.

#define DEVICE_QUANTITY 4  // Range: 1–8, adjust based on hardware configuration

Channel Mapping

The following table illustrates the relationship between DEVICE_QUANTITY and the enabled ADC channels:

DEVICE_QUANTITY

Active ADC Channels

1

AFCI_CH1

2

AFCI_CH1, AFCI_CH2

3

AFCI_CH1, AFCI_CH2, AFCI_CH3

4

AFCI_CH1, AFCI_CH2, AFCI_CH3, AFCI_CH4

5

AFCI_CH1, AFCI_CH2, AFCI_CH3, AFCI_CH4, AFCI_CH5

6

AFCI_CH1, AFCI_CH2, AFCI_CH3, AFCI_CH4, AFCI_CH5, AFCI_CH6

7

AFCI_CH1, AFCI_CH2, AFCI_CH3, AFCI_CH4, AFCI_CH5, AFCI_CH6, AFCI_CH7

8

AFCI_CH1, AFCI_CH2, AFCI_CH3, AFCI_CH4, AFCI_CH5, AFCI_CH6, AFCI_CH7, AFCI_CH8

Benefits:

  • Dynamic channel selection for flexible I/O configuration

  • Scalable architecture supporting 1–8 channels

  • Optimized resource utilization based on hardware requirements

Build Configuration

The project supports two build configurations optimized for different development and deployment scenarios:

Option A: Development with Algorithm Library

This configuration links the device application with the algorithm library into a single executable binary.

Advantages:

  • Enhanced debugging capabilities with full application access

  • Improved MCU cache efficiency through unified memory layout

  • Reduced memory footprint for resource-constrained embedded systems

  • Single firmware image

Use Cases:

  • Active algorithm development and optimization

  • System-level debugging and profiling

  • Performance tuning and cache optimization

Option B: Development with Algorithm Binary

This configuration utilizes a pre-compiled algorithm binary, maintaining separation between the application and algorithm components.

Advantages:

  • Modular architecture enabling independent updates

  • Algorithm updates without a full application rebuild

  • Reduced compilation time during application development

  • Simplified version management for algorithm components

  • Enhanced IP protection for proprietary algorithms

Use Cases:

  • Production deployments with stable algorithms

  • Field updates requiring only algorithm modifications

  • Multi-team development with separate responsibilities

Configuration Selection:

In MCUXPresso IDE:

  1. Right-click on the project in the Project Explorer.

  2. Navigate to Build Configurations → Set Active.

  3. Select your preferred configuration.

Project Configuration Settings

Memory Configuration (Optional)

Users can reallocate the SRAM memory depending on their application requirements. By default, the SRAM memory assignment is shown below. The BINARY_DATA region is used for the data memory of the algorithm binary generated from Time Series Studio.

Memory Layout

Hardware Setup

  • Connect the SWD interface (J6) on the target board to the PC using an external debugger (e.g., J-Link debugger).

  • Connect the target board to the PC via the USB port labeled FSUSB, which supports both power supply and serial communication for basic logging and prediction results display. Connect the USB port labeled HSUSB to send sensor data from the board to the PC when using the data logging or auto-labeling function in Time Series Studio.

  • Set up the AFCI test system and connect the current sensor to the target board.

Firmware and Model Deployment

Firmware Deployment

Program the firmware onto the target board.

Algorithm Deployment

  • Library deployment: Update the library file in the ./source/model directory. Rebuild the project and flash the firmware onto the target board.

  • Binary deployment: Program the algorithm model binary generated from Time Series Studio onto the target board using Time Series Studio or another flash tool. The flash address for programming is 0x100000 by default.

Execution and Monitoring

Open the serial terminal in MCUXpresso IDE for basic logging and prediction results display.

Serial Port

Summary

This guide outlines the configuration and deployment of the TSS AFCI application on MCXN547, enabling:

  • Real-time arc fault detection

  • Scalable multi-channel acquisition

  • Flexible deployment with library or binary models

The system is designed to support both development efficiency and production scalability.