I am looking for a solution for a cohesive predictive maintenance application using continuous, real-time streaming of data that is continuously monitored for any changes. I was looking to use the industrial grade digital MEMS microphone (IMP34DT05), analog MEMS microphone with frequency response up to 80 kHz (IMP23ABSU) and/or ultra-wide bandwidth (up to 6 kHz), low-noise, 3-axis digital vibration sensor (IIS3DWB) on the STEVAL-STWINKT1B board.
The goal is to have data continuously streamed via BLE or Wifi to the cloud, preferably AZURE. Whether it is though edge-processing (preferred) or any other data analysis, the end goal is to be able to continuously monitor this data and detect any anomalies or changes in the data that would indicate a maintenance issue. A possible audio application is FFT analysis and determining if a frequency is out of the expected range.
From my understanding, X-CUBE-MEMSMIC1 and FP-SNS-DATALOG1 only have capabilities to collect / monitor data that has been saved to the SD card or streamed through USB. I am wondering if there is a way to incorporate X-CUBE-AZURE / X-CUBE-AZRTOS-L4 into the stand-alone/handmade projects in those software expansions to create a project I can modify for my own application after purchasing the STEVAL-STWINWFV1 board for Wifi compatibility.
Alternatively, I have looked into using FP-AI-MONITOR1 with either the X-CUBE-AI or NanoEdgeAI application. I am wondering which implementation would be better suited for continuous monitoring of the desired sensors. I have looked a bit at the X-CUBE-AI documentation but am not sure how to properly implement it yet.
I know ST supplies a function pack (FP-IND-PREDMNT1) that could potentially be useful in this scenario, but I am turned off by the need for DSH-PREDMNT and continuous payment for its use, and the inability to implement hand-made code in the project's execution.
Thank you.
