The present invention relates to an equipment diagnosis method using equipment diagnosis system comprising: an imaging module (110) for collecting image data by photographing the equipment having an equipment controller, in which a PLC is loaded, embedded therein; a diagnostic module (120) including hardware having software for diagnosing whether the equipment is normal or abnormal; and a plurality of IoT sensor units (130) for monitoring an object to be monitored, and thus a user can quickly diagnose, identify, and cope with a specific cause of an equipment failure on the basis of objective data provided from a PLC memory area, and image file, and an IOT sensor unit at the occurrence of various types of events generated by a diagnostic module for each condition designated by the user according to the state of equipment.

Process control of continuous production of biomolecules is a major challenge due to complex nature of processes and time scales of operations involved. Availability of key process variables in real-time is one of main requirements. This disclosure relates to a processor implemented method of controlling a continuous bioprocessing plant which includes at least one of: receiving, an input data associated with one or more equipments; generating, by a recipe builder, a sequence of unit operations to determine at least one job order based on the at least input data; obtaining, a control decision associated with a control parameter based on the at least one job order; communicating, via the middleware, the control decision associated with the control parameter to the PLC; and executing, by a control system of the PLC, the control decision on a unit equipment to control: (i) a continuous bioprocessing train, and (ii) an individual unit operation.

The invention relates to a method which is used to operate an injection molding machine for processing plastics, which has a mold closing unit (F) for opening and closing an injection mold (M) having at least one mold cavity (12) in order to produce an injection molded part (13), an injection molding unit (S) for plasticizing and injecting new plasticizable material into the mold cavity (12), and a control system (11) for operating the injection molding machine (10). Stored in the control system is expert knowledge (E) about the operation of the injection molding machine and the peripheral devices (P) of the latter which may possibly be present and about the production of injection molded parts (13) using injection molding technology, in order to produce an injection molded part (13) using interactive contact as needed by an operator by using injection molding parameters. The fact that, in further steps, information about the component or the mold cavity (12) is provided to the control system (10) means that the plant and process parameters required for the production of the injection molded part (13) can be calculated by the control system (10) before the first injection molded part is produced, such that an alternative procedure for the operator-friendly setting of a machine for processing plastics is made available.

In example implementations described herein, the power of time series machine learning is used to extract the statistics of Programmable Logic Controller (PLC) data and external sensor data. The accuracy of time series machine learning is improved by manufacturing context-dependent segmentation of the time series into states which is factory may be in. The invention can capture subtle trends in these time series data and be able to classify them into several outcomes from ICS security attacks to normal anomalies and machine/sensor failures.

The present invention relates to an equipment diagnosis method using equipment diagnosis system comprising: an imaging module (110) for collecting image data by photographing the equipment having an equipment controller, in which a PLC is loaded, embedded therein; a diagnostic module (120) including hardware having software for diagnosing whether the equipment is normal or abnormal; and a plurality of IoT sensor units (130) for monitoring an object to be monitored, and thus a user can quickly diagnose, identify, and cope with a specific cause of an equipment failure on the basis of objective data provided from a PLC memory area, and image file, and an IOT sensor unit at the occurrence of various types of events generated by a diagnostic module for each condition designated by the user according to the state of equipment.

A method of operating an intelligent programmable logic controller over a plurality of scan cycles includes the intelligent programmable logic controller executing a control program and one or more reasoning algorithms for analyzing data received and transmitted by the intelligent programmable logic controller. The intelligent programmable logic controller receives one or more user-specified declarative knowledge models from an external source via a deployment interface included in the intelligent programmable logic controller. The intelligent programmable logic controller dynamically modifies the reasoning algorithms during runtime of the control program based on the user-specified declarative knowledge models.

Process control of continuous production of biomolecules is a major challenge due to complex nature of processes and time scales of operations involved. Availability of key process variables in real-time is one of main requirements. This disclosure relates to a processor implemented method of controlling a continuous bioprocessing plant which includes at least one of: receiving, an input data associated with one or more equipments; generating, by a recipe builder, a sequence of unit operations to determine at least one job order based on the at least input data; obtaining, a control decision associated with a control parameter based on the at least one job order; communicating, via the middleware, the control decision associated with the control parameter to the PLC; and executing, by a control system of the PLC, the control decision on a unit equipment to control: (i) a continuous bioprocessing train, and (ii) an individual unit operation.

A method of operating an intelligent programmable logic controller over a plurality of scan cycles includes the intelligent programmable logic controller executing a control program and one or more reasoning algorithms for analyzing data received and transmitted by the intelligent programmable logic controller. The intelligent programmable logic controller receives one or more user-specified declarative knowledge models from an external source via a deployment interface included in the intelligent programmable logic controller. The intelligent programmable logic controller dynamically modifies the reasoning algorithms during runtime of the control program based on the user-specified declarative knowledge models.