Abstract: Example techniques for processing service notes are described. In an example, labeled service notes, associated with fuser units of a plurality of image rendering devices, are processed to generate a vector corresponding to each of the labeled service notes, a labeled service note comprising natural language text describing an error event and a corresponding service activity associated with a fuser unit, wherein the labeled service note is assigned a label based on a category of failure of the fuser unit. Based on the processing, a relationship between vectors and labels corresponding to the labeled service notes is generated.

Abstract: According to examples, an apparatus may include a processor that may receive an environmental condition detected at an electronic device and may determine whether the received environmental condition is outside of a predefined threshold range for the electronic device. The processor may, based on a determination that the received environmental condition is outside of the predefined threshold range for the electronic device, generate a notification item in a configuration page of the electronic device indicating that the electronic device has operated in an environmental condition that is outside of the predefined threshold range for the electronic device. The processor may also store the generated notification item in the configuration page of the electronic device, in which a service agent of the electronic device is to access the notification item in the configuration page stored in the electronic device.

Abstract: A method, comprising receiving telemetry data from a plurality of computing devices, the telemetry data including data obtained during setup of the plurality of computing devices. The method also includes receiving usage data pertaining to the plurality of computing devices, the usage data including data obtained during setup of the plurality of computing devices. The method includes receiving return data pertaining to a subpart of the plurality of computing devices, correlating the received telemetry data and the received usage data with the received return data. The method further includes extrapolating return data for a remainder of the plurality of computing devices based on the correlation between the received telemetry data, the received usage data, and the received return data.

Abstract: An apparatus may include a processor that may be caused to access a plurality of measurements of a device. The processor may provide the plurality of measurements as an input to an autoencoder, the autoencoder being trained based on measurements of devices in working condition and access an output of the autoencoder, the output comprising a reconstruction of the input based on decoding an encoded version of the input. The processor may further be caused to determine whether the device will fail based on the output.

Abstract: An example non-transitory computer-readable storage medium comprises instructions executable by a processor to receive data including event log data and repair event data indicative of failures of the plurality of information technology (IT) devices, generate a plurality of images corresponding to the plurality of event codes of the event log data, and train a predictive data model using the plurality of images as inputs to the predictive data model and the repair event data as known outputs. For example, the predictive data model is trained to identify a plurality of failure windows and operational windows associated with the plurality of IT devices based on the repair event data and the event log data, and classify a plurality of sliding windows associated with the plurality of images based on the plurality of failure windows and operational windows.

Abstract: Example implementations relate to audio samples to detect device anomalies. For example, computing device, comprising: a processing resource and a non-transitory computer readable medium storing instructions executable by the processing resource to: generate a matrix of audio information for a plurality of audio samples of a device, select audio information from one of the plurality of audio samples, generate a plurality of principal components for the selected audio information utilizing a principal component expansion, select a principal component from the plurality of principal components based on a quantity of variance, and detect an anomaly of the device based on a comparison between a real time audio sample of the device and the selected principal component.

Abstract: Example techniques for processing service notes are described. In an example, labeled service notes, associated with fuser units of a plurality of image rendering devices, are processed to generate a vector corresponding to each of the labeled service notes, a labeled service note comprising natural language text describing an error event and a corresponding service activity associated with a fuser unit, wherein the labeled service note is assigned a label based on a category of failure of the fuser unit. Based on the processing, a relationship between vectors and labels corresponding to the labeled service notes is generated.

Abstract: A device monitoring system comprising a computation engine to obtain, for each of a plurality of devices, an actual failure condition indicating actual device failure and a probable failure condition predicted by a health monitoring device. The health monitoring device to monitor health of the plurality of devices, the probable failure condition indicating when the device is predicted to stop functioning. The computation engine is to compute a failure prediction gap for each of the plurality of devices. The failure prediction gap indicating a difference between the probable failure condition and the actual failure condition. A performance evaluation engine to compute a saving factor based at least on cost parameters and an average of the failure prediction gap computed for the plurality of devices and initiate discontinuance of usage of the health monitoring device based on a comparison of the saving factor with a threshold.