Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Methods, non-transitory computer readable media, and work order analysis server devices are disclosed that train a machine learning model based on historical invoice data and target quote rates. A baseline quote rate for an enterprise is generated based historical invoices, quotes, and NTE values. A target quote rate is generated based on the baseline quote rate, tolerance data for the enterprise, and first business rule(s). The tolerance data includes a quantitative indication of an efficiency preference of the enterprise with respect to work order review. The machine learning model is then applied to the target quote rate and work order data extracted from an NTE limit request received from the enterprise to generate a model-recommended NTE limit. A prescribed NTE limit is returned in response to the NTE limit request, which is generated based on an application of second business rule(s) to the model-recommended NTE limit.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Methods, non-transitory computer readable media, and an apparatus that assess an impact of a change in a scenario on workplace management include receiving an identification of a workplace environment and a selection of one of a plurality of types of scenarios associated with one of a plurality of types of workplace management machine learning models from one of a plurality of client devices. A subset of stored workplace environment data is retrieved based on the identification of the workplace environment and one or more inputs for the one of workplace management machine learning models associated with the selected one of the scenarios. One or more simulations are executed based one on more received changes in the retrieved subset of workplace environment data in the selected one of the types of workplace management machine learning models to generate a set of insight data. The generated set of insight data for the workplace environment is output to the one of the client devices.

Abstract: Some embodiments provide a program that retrieves a set of notifications describing failures that occurred on a set of monitored devices. The program further determines a set of topics based on the set of notifications. The program also determines failure modes associated with the set of topic from a plurality of failure modes defined for the set of monitored devices. The program further determines failure modes associated with the set of notifications based on the set of topics and the failure modes associated with the set of topics. The program also receives a particular notification that includes a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices. The program further determines a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

Abstract: Techniques for implementing and using failure curve analytics in an equipment maintenance system are disclosed. A method comprises: accessing a failure curve model for an equipment model, the failure curve model being configured to estimate lifetime failure data for the equipment model for different failure modes corresponding to different specific manners in which the equipment model is capable of failing, the lifetime failure data indicating a probability of the equipment model failing in the specific manner of the failure mode; generating first analytical data for a first failure mode of the plurality of failure modes using the failure curve model based on the first failure mode, the first analytical data indicating at least a portion of the lifetime failure data for the equipment model corresponding to the first failure mode; and causing a visualization of the first analytical data to be displayed on a computing device.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

Abstract: Some embodiments provide a program that retrieves a set of notifications describing failures that occurred on a set of monitored devices. The program further determines a set of topics based on the set of notifications. The program also determines failure modes associated with the set of topic from a plurality of failure modes defined for the set of monitored devices. The program further determines failure modes associated with the set of notifications based on the set of topics and the failure modes associated with the set of topics. The program also receives a particular notification that includes a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices. The program further determines a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

Abstract: Methods and systems may provide an alignment scheme for components that may reduce positional deviation between the components. The method may include placing a first component on top of a substrate, wherein the first component includes a receiving alignment feature, and coupling a second component to the first component, wherein the coupling includes inserting a protruding alignment feature of the second component into the receiving alignment feature of the first component. In one example, the first component includes an edge-emitting semiconductor die and the second component include one or more of an optical lens and an alignment frame.

Abstract: Extruded polystyrene foams are made with an added brominated styrene-butadiene polymer as a flame retardant. The blowing agent is a mixture of carbon dioxide, ethanol and water, which may also contain a C4-C5 hydrocarbon. The blowing agent mixture overcomes a tendency of the brominated styrene-butadiene to form very small cells. This allows the foam to expand fully to form good quality, low density extruded foam.

Abstract: Methods and systems may provide an alignment scheme for components that may reduce positional deviation between the components. The method may include placing a first component on top of a substrate, wherein the first component includes a receiving alignment feature, and coupling a second component to the first component, wherein the coupling includes inserting a protruding alignment feature of the second component into the receiving alignment feature of the first component. In one example, the first component includes an edge-emitting semiconductor die and the second component include one or more of an optical lens and an alignment frame.

Abstract: In an embodiment, a method of providing an arranged display of data associated with a set of time periods is presented. In this method, values of a first data type are accessed, the values being observed during each of multiple time periods. An order for the time periods is determined based on the values of the first data type. A selectable region for each of the time periods is displayed, the regions being arranged according to the order. In response to a user selection of one of the selectable regions, a value of a second data type is displayed, the value of the second data type being observed during the time period of the selected one of the selectable regions.

Abstract: Methods and systems may provide an alignment scheme for components that may reduce positional deviation between the components. The method may include placing a first component on top of a substrate, wherein the first component includes a receiving alignment feature, and coupling a second component to the first component, wherein the coupling includes inserting a protruding alignment feature of the second component into the receiving alignment feature of the first component. In one example, the first component includes an edge-emitting semiconductor die and the second component include one or more of an optical lens and an alignment frame.