Abstract: A method for classifying assets in a facility includes receiving item point metadata and time series data associated with an asset, applying a machine learning neural network to the item point metadata and time series data to determine a similarity between metadata associated with the asset and metadata associated with a known asset type, and determining that the asset is of the asset type based on the similarity satisfying a predetermined threshold. The neural network comprises anchor-based learning trained on a plurality of mined triplets including an anchor input, a positive input, and a negative input. The neural network is configured to generate vector representations associated with each of the anchor input, the positive input, and the negative input. A loss function applied to the vector representations is configured to differentiate between the vector representation associated with the positive input and the vector representation associated with the negative input.

Abstract: Methods, apparatuses, and computer program products for energy-centric predictive maintenance scheduling are provided. For example, a computer-implemented method may include inputting historical time-varying sensor state values associated with an asset into a data model to train the data model; inputting expected future time-varying asset-independent data over a time frame into the data model; generating from the data model predicted sensor state values and energy usage associated with the asset over the time frame; determining optimum energy usage by the asset over the time frame; calculating energy wastage over the time frame based on a difference between the predicted energy usage and the optimum energy usage; calculating, using the predicted sensor state values, one or more asset performance metrics corresponding to one or more preventive maintenance tasks; and generating and reporting one or more recommended service tasks over the time frame based at least in part on the calculated energy wastage.

Abstract: Various embodiments described herein relate to an anomaly detection framework adaptable to different asset types. In this regard, a data stream associated with a first asset is received. The data stream is then processed to generate output data by encoding the data stream based on historical data associated with the first asset, the historical data comprising clustered data representative of fault states and one or more non-fault states. Furthermore, in accordance with a determination that the generated output data is indicative of a potential fault of the first asset, fault data indicative of the potential fault is generated and caused to be transmitted to an administrative device for display.

Abstract: Various embodiments described herein relate to an anomaly detection framework adaptable to different asset types. In this regard, a data stream associated with a first asset is received. The data stream is then processed to generate output data by encoding the data stream based on historical data associated with the first asset, the historical data comprising clustered data representative of fault states and one or more non-fault states. Furthermore, in accordance with a determination that the generated output data is indicative of a potential fault of the first asset, fault data indicative of the potential fault is generated and caused to be transmitted to an administrative device for display.

Abstract: Various embodiments described herein relate to providing optimization related to enterprise performance management. In this regard, a request to obtain one or more insights with respect to a formatted version of disparate data associated with one or more data sources is received. The request includes an insight descriptor that describes a goal for the one or more insights. In response to the request, aspects of the formatted version of the disparate data is associated to provide the one or more insights. The associated aspects are determined by the goal and relationships between the aspects of the formatted version of the disparate data. Furthermore, one or more actions are performed based on the one or more insights.