From 2d5f3f10c45539b49e2c962766a163cc264cfe09 Mon Sep 17 00:00:00 2001 From: david Date: Thu, 4 Apr 2024 13:33:27 -0700 Subject: [PATCH] machine image --- .../{heavy-machinery.jpg => heavy-machines.jpg} | Bin .../compressor-audio-classification-rasynboard.md | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename .gitbook/assets/compressor-audio-classification-rasynboard/{heavy-machinery.jpg => heavy-machines.jpg} (100%) diff --git a/.gitbook/assets/compressor-audio-classification-rasynboard/heavy-machinery.jpg b/.gitbook/assets/compressor-audio-classification-rasynboard/heavy-machines.jpg similarity index 100% rename from .gitbook/assets/compressor-audio-classification-rasynboard/heavy-machinery.jpg rename to .gitbook/assets/compressor-audio-classification-rasynboard/heavy-machines.jpg diff --git a/audio-projects/compressor-audio-classification-rasynboard.md b/audio-projects/compressor-audio-classification-rasynboard.md index 6ff27fe2..a284b05e 100644 --- a/audio-projects/compressor-audio-classification-rasynboard.md +++ b/audio-projects/compressor-audio-classification-rasynboard.md @@ -13,7 +13,7 @@ Public Project Link: [https://studio.edgeimpulse.com/public/343756/latest](https Most industrial settings such as pumping facilities, heating, ventilation, and air conditioning (HVAC) machinery, datacenter infrastructure rooms, manufacturing and heavy industry sites will have proprietary, expensive, critical machinery to maintain. On-site workers with experience near the machinery or equipment, are generally quick to identify when a machine doesn't seem to "sound right", "feel right", or "look right". Previous experience helps them to understand the "normal” state of the equipment, and this intuition and early warning can allow for scheduled repairs or proper planning for downtime. However, facilities that don't have 24/7 staffing or on-site workers could be more prone to equipment failures, as there is no one to observe and take action on warning signs such as irregular sounds, movements, or visual indicators. -![](../.gitbook/assets/compressor-audio-classification-rasynboard/heavy-machinery.jpg) +![](../.gitbook/assets/compressor-audio-classification-rasynboard/heavy-machines.jpg) Predictive Maintenance using machine learning aims to solve for this problem, by identifying and acting upon anomalies in machinery health.