In contrast, embodiments of the present disclosure may be able to proactively detect and correct faults occurring in industrial (e.g., HVAC) equipment. For instance, monitoring the equipment using audio may provide an early indication of a fault occurring in the equipment (e.g., before the fault causes a significant problem), such that the fault may be detected and/or corrected with minimal or no downtime in the equipment. Further, embodiments of the present disclosure may be able to automatically detect and/or correct a fault occurring in the equipment (e.g., the fault may be detected and/or corrected without a technician having to visit the site and manually inspect the equipment). Further, embodiments of the present disclosure may facilitate the learning of fault patterns over time via crowd sourcing for identification of anomalous audio, such that future faults can be accurately detected and corrected across multiple facilities in a quicker and more efficient manner. For instance, embodiments of the present disclosure may not have to be pre-configured; rather, embodiments can be taught to recognize unique patterns for the environment of the system. Further, embodiments of the present disclosure can self-learn the noise profile and/or characterization of the equipment upon deployment. Further, embodiments of the present disclosure can facilitate the capture and distribution of expert technician knowledge across similar equipment in different facilities.

In the following detailed description, reference is made to the accompanying drawings that form a part hereof. The drawings show by way of illustration how one or more embodiments of the disclosure may be practiced.