A system that allows a contractor to remotely monitor and/or interact with its customers' building control systems, such as heating, ventilating and air conditioning (HVAC) systems, and analyze information obtained from the building control systems over time. Such a system may help the contractor monitor and diagnosis customer building control systems, setup service calls, achieve better customer relations, create more effective marketing opportunities, as well as other functions. In some cases, the disclosed system may be configured to allow a user to grant or deny access to its HVAC system in response to the user receiving an electronic invitation to the system. The granting of access by a user to its HVAC system may allow for remote monitoring of the HVAC system.

A method includes acquiring a current condition indicator of a condition indicator set associated with an operating condition of a vehicle, the condition indicator set indicating sensor readings associated with an operating element of the vehicle under the operating condition, determining, by a data server, a volatility over a window of the condition indicator set based, at least in part, on differences between adjacent data points in the window, wherein the window includes the current condition indicator, determining, by the data server, a movement over the window of the condition indicator set based, at least in part, on a difference between two selected data points in the window, determining a volatility-based movement significance over the window of the condition indicator set based, at least in part, on a ratio of the movement to the volatility, determining, by the data server, whether a trend associated with the operating element is indicated based, at least in part, on the volatility-based movement significance, and generating, by the data server, an alert signal in response to the determining that the trend is indicated.

A production system, a drive and an operating method provide the functions of quality measurement and mechanism diagnosis. The production system has a hierarchical processing structure. The drive includes a real system driving module and a virtual system driving module. The real system driving module generates a mechanism real operation parameter information. In a system identification mode, the drive creates at least one virtual mechanism model. The drive generates a mechanism stimulation operation parameter information according to a processing strategy of a controller, the mechanism real operation parameter information and the at least one virtual mechanism model. The controller performs a quality measurement operation, performs a mechanism diagnosis operation and/or adjusts the processing strategy according to the mechanism real operation parameter information and the mechanism stimulation operation parameter information.

A system that allows a contractor to remotely monitor and/or interact with its customers' building control systems, such as heating, ventilating and air conditioning (HVAC) systems, and analyze information obtained from the building control systems over time. Such a system may help the contractor monitor and diagnosis customer building control systems, setup service calls, achieve better customer relations, create more effective marketing opportunities, as well as other functions. In some cases, the disclosed system may include a controller that analyzes data from HVAC systems, determines a thermal model of a space environmentally controlled by an HVAC system, and provides an energy audit of the space that is environmentally controlled by the HVAC system. The controller may output a result of the energy audit to a user.

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

A computer-implemented method to control technical equipment that performs a production batch-run of a production process, the technical equipment providing data in a form of time-series from a set of data sources, the data sources being related to the technical equipment, includes: accessing a reference time-series with data from a previously performed batch-run of the production process, the reference time-series being related to a parameter for the technical equipment; and while the technical equipment performs the production batch-run: receiving a production time-series with data, identifying a sub-series of the reference time-series, and comparing the received time-series and the sub-series of the reference time-series, to provide an indication of similarity or non-similarity, in case of similarity, controlling the technical equipment during a continuation of the production batch-run, by using the parameter as control parameter.

A communication system and method that provides predictive and prescriptive analytics for a system running at an edge. In one embodiment, the communication system includes an architect subsystem configured to build, test and deploy a model based on sensor characteristics of the system. The sensor characteristics are from at least one of an operator input, a historical input, a specification input, and a subject-matter expert input. The communication system also includes an edge subsystem configured to receive said model and perform predictive and prescriptive analytics on sensor data from said system running on said model deployed at said edge.

Provided is a defect occurrence prediction system for a machine tool that makes it possible to identify the factors causing the occurrence of defects efficiently and effectively, and predict the occurrence of the defects accurately with good precision. A defect occurrence prediction system includes an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool; a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time.

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

A method for detecting a fault includes: receiving a plurality of time-series sensor data obtained in one or more manufacturing processes of an electronic device; arranging the plurality of time-series sensor data in a two-dimensional (2D) data array; providing the 2D data array to a convolutional neural network model; identifying a pattern in the 2D data array that correlates to a fault condition using the convolutional neural network model; providing a fault indicator of the fault condition in the one or more manufacturing processes of the electronic device; and determining that the electronic device includes a fault based on the fault indicator. The 2D data array has a dimension of an input data to the convolutional neural network model.