BACKGROUND

The various embodiments described herein generally relate to device analysis via artificial intelligence. More specifically, the various embodiments relate to maintenance of a plurality of devices using machine learning techniques.

Idling of devices in a client ecosystem for extended durations, whether through client absence or other reasons of client unavailability, may impact the condition of such devices. Degradation during idle cycles is a significant issue that may adversely affect device longevity. During an idle cycle, deteriorating factors may impact lifespan of device components. For instance, battery discharge issues may arise as a result of excessive idle cycle length. Additionally, idle cycle degradation may be exacerbated due to device wear factors such as dust or moisture and/or environmental exposure factors such as temperature, humidity, or precipitation.

SUMMARY

The various embodiments described herein provide techniques of managing device maintenance via artificial intelligence, specifically machine learning. An associated computer-implemented method includes constructing a machine learning knowledge model associated with a plurality of devices in a client ecosystem based upon at least one course of device evaluation. The method further includes collecting, from a plurality of device sensors, device sensor data associated with each of the plurality of devices and collecting, from a plurality of environmental sensors, environmental sensor data associated with each of the plurality of devices. The method further includes analyzing, via the machine learning knowledge model, the device sensor data in view of the environmental sensor data to determine a failure risk value for each of the plurality of devices. Responsive to the failure risk value for any device among the plurality of devices exceeding a predefined failure risk threshold, the method further includes facilitating activation of the device for a designated duration of time.

In an embodiment, the step of constructing the machine learning knowledge model includes storing in at least one knowledge base operational information for each of the plurality of devices during the at least one course of device evaluation and storing in the at least one knowledge base environmental information associated with each of the plurality of devices during the at least one course of device evaluation. According to such embodiment, the step of constructing the machine learning knowledge model further includes applying at least one artificial intelligence algorithm to train the machine learning knowledge model based upon the stored operational information and the stored environmental information. The at least one artificial intelligence algorithm optionally includes at least one clustering algorithm that generates at least one output reflecting failure trends with respect to the plurality of devices. Furthermore, the machine learning knowledge model optionally is a multi-agent reinforcement learning model. According to such embodiment, the at least one artificial intelligence algorithm includes a multi-agent reinforcement learning (MARL) algorithm that applies multi-objective optimization to the plurality of devices. Additionally, the multi-agent reinforcement learning model optionally is trained in conjunction with a long short-term memory recurrent neural network (LSTM-RNN) architecture configured to store time series pattern data with respect to the plurality of devices.

In an embodiment, the step of facilitating activation of the device includes accessing a dataset enumerating at least one maintenance action with respect to the device, selecting one or more actions among the at least one maintenance action in the dataset based upon the failure risk value and the analysis of the device sensor data in view of the environmental sensor data, and facilitating initiation of the selected one or more actions. In another embodiment, the step of facilitating activation of the device includes facilitating charging of a battery component associated with the device. In a further embodiment, the step of facilitating activation of the device includes initiating operation of at least one physical component of the device. The designated duration of time optionally is determined at least in part based upon the device sensor data associated with the device. Additionally or alternatively, the designated duration of time optionally is determined at least in part based upon the environmental sensor data associated with the device.

One or more additional embodiments pertain to a computer program product including a computer readable storage medium having program instructions embodied therewith. According to such embodiment(s), the program instructions may be executable by a computing device to cause the computing device to perform one or more steps of and/or to implement one or more embodiments associated with the above recited computer-implemented method. One or more further embodiments pertain to a system having a processor and a memory storing an application program, which, when executed on the processor, performs one or more steps of and/or implements one or more embodiments associated with the above recited computer-implemented method.

