A system may comprise a device. The device may be configured to receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; and predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components. The machine learning model is trained, using training data, to predict the wear rate of the one or more components. The training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices. The device may perform an action based on the amount of wear.

A method for predicting end-of-life for a component includes determining a baseline lifetime model for a component connected to a machine functional safety system. The component is part of a system with physical devices. The method includes monitoring environmental conditions and usage conditions of the component and modifying the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method includes tracking a lifetime progress of the component with respect to the modified lifetime model and sending an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model.

A method for automatically adapting a predictive model used to control a heating, ventilation, or air conditioning (HVAC) system in a building to compensate for a detected fault in the HVAC system is shown. The method includes obtaining an indication of the detected fault in the HVAC system or a zone in the building. The method further includes determining a predicted impact of the detected fault on an operational performance of the HVAC system. The method further includes adjusting one or more parameters of the predictive model based on the predicted impact of the detected fault to generate a fault-adapted predictive model. The method further includes operating the HVAC system to control an environmental condition of the building using the fault-adapted predictive model.

A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

Systems and methods for predicting component failure in a vehicle and alerting a driver based thereon. In an embodiment, the method includes receiving sensor data from at least one vehicle sensor over a period of time, processing the sensor data using a predictive model to detect an anomaly indicative of an upcoming component failure, determining a severity of the upcoming component failure based on at least one operating characteristic of the vehicle, and providing an alert to the driver of the vehicle regarding the severity of the upcoming component failure.

A diagnostic system diagnoses an abnormality of a diagnostic object by using a plurality of diagnostic logics. The diagnostic system includes an acquisition unit, a determination unit, a generation unit, and an output unit. The acquisition unit acquires diagnostic object information relating to the diagnostic object. The determination unit determines whether each of the diagnostic logics is executable or not based on the diagnostic object information. The generation unit generates a diagnostic result including information relating to whether the diagnostic logics determined by the determination unit are executable or not. The output unit outputs the diagnostic result.

A method for the predictive maintenance of an automatic machine for manufacturing or packing consumer articles comprising the steps of: detecting and recording at least a sampling series relating to at least one motorization metric of at least one electric actuator, by means of at least one respective local control unit; transmitting the recorded sampling series to a data processing unit; defining at least one multidimensional tolerance horizon within an anomaly matrix having as dimensions at least two statistical features based on at least one sampling series detected and relative at least to the detected motorization metric; calculating the two statistical features in order to define the position of an actual condition within the anomaly matrix; determining, based on the position of the actual condition in the anomaly matrix and the multidimensional tolerance horizon, the imminence of necessary maintenance.

A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.

A system including at least one computer and code executable thereby for implementing a mechanism for monitoring performances of applications of an application chain. The system includes an arrangement forming a measuring repository on the one hand for measuring levels of use of resources of applications during periods of degradation of performances of the applications, and by application and by period of the application chain, in a memory storing these levels of use. The arrangement is further operable to: establish a repository of use data by defining and storing in at least one memory, by resource and by application, thresholds of acceptable performance of the level of use of the measuring repository; constitute a categorization module of performance problems as a function of measuring and use repositories; and implement an alert mechanism when the monitoring mechanism detects a performance problem of the applications or when the problem is resolved.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.