A monitoring and inspection system for a work area includes a non-visual sensory detection sensor, such as a microphone or vibration detection sensor, and a processor. The sensor is configured to sense sounds or vibrations generated in the work area during the performance of an action that are then received by the processor. The processor analyzes the received acoustic and/or vibrational signals and compares the received signals to an expected signal to verify that the identified acoustic and/or vibration signature of the detected signal is an acoustic and/or vibration signature associated with the operational step that was performed to confirm that the operational step has been performed, and that it has been performed properly.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

The invention relates to a system for controlling manual operations during production of an assembly of parts, the system comprising a portable hand tool allowing an assembly technician to perform manual operations on a plurality of elements of said assembly of parts, and locating means integrated at least in part into an electronic module attached to said portable hand tool and able to determine a location of at least one manual operation associated with an element by locating the position of said portable hand tool in a three-dimensional coordinate system associated with a set of modelling data representing a three-dimensional modelled image of said assembly of parts. According to the invention, said locating means comprise a depth camera belonging to said electronic module and a processing module able to estimate the position of said portable hand tool on the basis of images captured by said depth camera.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

A system and method for mounting a modular switch cabinet equipment in a switch cabinet housing (6) includes a computer-aided assistance unit (1) for determining a production-efficient assembly step sequence. The system and method further include at least one display unit (11-11′) for image and/or text information, which is installed at the assembly site for at least one fitter (M) in order to visualize manual assembly steps (A; B; D) of the determined assembly step sequence. The system and method further includes at least one input unit (12-12′) for acknowledgement of the completed assembly step (A; B; D) by the fitter (M).

A worker guidance system for guiding a worker when carrying out a process using a process system. The process system includes at least one process device having process device capabilities, the process including at least one work step, including a control unit and a communication device. The control unit is designed to activate the communication device based on process data to communicate to the worker the work step to be carried out. The control unit is provided with the process device capabilities as process device capability data, the process data including the process device capability data.

The present disclosure provides a prompt method and system for building a modular apparatus. The method comprises: S1: obtaining configuration information of a target modular apparatus which comprises M unit-modules connected to each other by a docking part; S2: obtaining configuration information of a currently constructed entity which comprises N unit-modules connected to each other by a docking part, N being less than M; S3: calculating at least a position of the docking part where the (N+1).sup.th unit-module should be connected to the constructed entity; and S4: sending out prompt information according to at least the position of the docking part where the (N+1).sup.th unit-module should be connected to the constructed entity calculated in step S3, so as to prompt at least the position of the docking part where the (N+1).sup.th unit-module should be connected.

A system for outputting a work instruction, in which an image processing device that takes a picture of a photographic subject is connected to an output device is disclosed. The system includes an imaging unit that generates image data showing the picture of the photographic subject; an obtaining unit that obtains determination data used for determining whether a work process performed on the photographic subject is completed; an input unit that inputs reference material data indicating reference materials used for reference in the work process; a determination unit that determines whether the work process performed on the photographic subject is completed, the determination being made depending on whether the photographic subject in the image data is in a state indicated by the determination data; and an output unit that outputs, to the output device, reference material data for a work process specified in accordance with the determination.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.

An initial customized electronic work scope is created for an individual industrial asset. The initial electronic work scope is created according to the set of rules defined in the tree-like data structure and uses the determined condition of the asset. The initial customized electronic work scope is not necessarily the same as other work scopes of other industrial assets the same asset type. The initial customized electronic work scope defines an ordered sequence for removing a plurality of individual mechanical parts of the individual asset.