A method and an apparatus for operating an automation system is provided. The method for operating an automation system includes the method steps of: providing a learning-based prediction model for the automation system trained by process data including context of an automation process, receiving information about current context of the automation process, verifying context change by comparing the current context to the context of said process data, in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, in the case of any concept drift re-training said model with post-drift process data, in the case of no context change testing for random concept drift not detected by verifying context change, in the case of any random concept drift extend the current context by using data comprising previous context changes, otherwise no further method steps are required.

A diagnostic system includes an acquirer configured to acquire current waveform data representing a waveform relating to a current supplied to a driving device of an apparatus and a determiner configured to determine a degree of abnormality in the apparatus from a varying portion of the waveform, the varying portion corresponding to a varying time period during which a rotation speed of the driving device increases or decreases.

Systems, devices, and methods including: receiving, by an interpreter component having a processor with addressable memory, a first state of a tool of a computer numerical control (CNC) machine; determining, by the interpreter component, a reward and a value of the reward based on the received first state, where the reward is at least one of: positive and negative; transmitting, by the interpreter component, a set of information comprising the determined reward and the value of the reward to an agent component; performing, by the agent component, at least one action to generate a tool path and to proceed to a second state, where the second state is combined with the first state; and determining, by the agent component, the generated tool path based on the determined reward and value associated with the at least one action.

A data intersection is assessed of data to be used between at least two parties. The data is to be used in an artificial intelligence (AI) application. Evaluation is performed of set of instructions required for the AI application, where the evaluation creates a modified set of instructions where operands are symbolically associated with corresponding privacy levels. Using the assessed data intersection and the modified set of instructions, a mapping is created from the data to operands with associated privacy metrics. The mapping treats overlapping data from the assessed data intersection differently from data that is not overlapping to improve privacy relative to without the mapping. The AI application is executed using the data to produce at least one parameter of the AI application. The at least one parameter is output for use for a trained version of the AI application. Apparatus, methods, and computer program products are described.

One or more techniques and/or systems are disclosed for detecting the presence or formation of a blockage in a sewer system using a machine learning (ML) model. The ML model may be trained using a supervised learning method; and may be used to analyze various inputs from a sewer system to automatically sense developing sewer blockages. The systems and techniques described herein may also indicate the type of blockage that is forming, such as silt accumulation; sediment buildup; ragging; root intrusion; fats, oils, and grease (FOG), etc.) prior to a full blockage to allow preventative cleaning operations.

A programmable manufacturing advisor includes an information unit that receives measurements for at least one parameter of each operation of a plurality of operations in the manufacturing process, and an analytics unit that determines a baseline performance metric for the manufacturing process based on the measurements of the at least one parameter. The programmable manufacturing advisor also includes an optimization unit that determines a recommended improvement action by determining a predicted performance metric for the manufacturing process based on an adjusted value of the at least one parameter and comparing the predicted performance metric to the baseline performance metric. The optimization unit also automatically presents the recommended improvement action to the operations manager.

Embodiments of this invention relate generally to detection systems and a method for chemical substance detection using UV fluorescence, specular reflectance, and artificial intelligence. In one example, a handheld detection system comprises single or multiple excitation light sources at discrete wavelengths operating in an ultraviolet portion of an electromagnetic spectrum. The single or multiple excitation light sources are operated intermittently, either all in concert or individually, at a frequency of about 100 Hz to 1000 Hz. Multiple detectors are configured as channels to operate at discrete wavelengths to detect a multiplicity of emissions produced by the excitation energy. A multi-channel electronic or software-implemented detector is synchronized in both phase and frequency with the excitation light sources so that a signal of interest is detected in the multiplicity of emissions.

A manufacturing support system may be provided. The manufacturing support system may comprise: an obtaining unit (IO) configured to obtain object data of an object to be manufactured; an artificial intelligence, Al, engine (20) configured to receive the object data as an input and to determine a hardware configuration of a manufacturing system for manufacturing the object with reference to information relating to available hardware for the manufacturing system; and an output unit (60) configured to output the determined hardware configuration.

The Relevance Based Digital Building [RBDB] invention describes a method and apparatus consisting of a hardware and software environment that creates a relevance based digital building intelligence system. RBDB defines a Network Lighting System [NLS] that delivers the network fabric for an array of intelligent luminaries configured with Internet of Things [IoT] devices. Layered on the NLS network fabric is a Digital Building Intelligence information architecture that enables the building to become Self-Aware with Digital Personas. The building can manage many of its own needs through operational optimization, Machine to Machine [M2M] and machine to stakeholder interactions. RBDB allows the stakeholders associated with the building to take on a multiplicity of digital personas enabling high relevance interactions with the building and each other through these digital intelligence representations. The digital building adapts to the occupants (stakeholders) preferences and requirements and empowers he interaction between individuals (stakeholders) and their environment (Digital Building Intelligence).

Various embodiments include a gateway for transforming a data model of a manufacturing process equipment into an industrial information model for automation purposes. The gateway may include: a parsing module for parsing information entities in the data model of the manufacturing process equipment applying a communication protocol of the data model; a semantic mapping engine for transforming the information entities into the industrial information model, the semantic mapping engine using a runtime environment for executing knowledge-based mapping rules; an interface module for providing access to the industrial information model; a mapping knowledge unit including a mapping rule base for maintaining one or more declarative mapping rules; and a mapping compiler for transforming the declarative mapping rules into executable code, for compiling the executable code into knowledge-based mapping rules and for feeding the knowledge-based mapping rules into the runtime environment of the semantic mapping engine.