Described are techniques for artificial intelligence (AI) assisted recommendations for component parts for end-products that reduce the occurrence of a product failures. The techniques include obtaining measurement data for groups of component parts configured to be assembled as part of an end-product. The techniques further include obtaining specification scores for the component parts included in the groups of component parts based on the measurement data. The techniques further include selecting a component part combination from the groups of component parts using artificial intelligence analysis of the specification scores to determine that the component part combination decreases a probability of a product failure as compared to historical occurrences of the product failure. The techniques further include outputting information for at least one component part included in the component part combination.

A system for monitoring a technical device. The system has a capturing means for reading in a first visual representation containing information regarding a setting parameter and/or a measurement parameter of a controller to control the technical device. The setting parameter and/or the measurement parameter characterizes a state of the technical device. The system may have a scanning means for detecting a symbol in the visual representation; a database for associating the setting parameter and/or the measurement parameter with the detected symbol; an interface to the controller to capture a value of the setting parameter and/or of the measurement parameter of the controller; and an image generation means for generating a second visual representation coupled to the controller such that information of the setting parameter and/or the measurement parameter is displayed with the first visual representation. The system has an image display means for presenting the generated second visual representation.

Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

A method and apparatus for data analysis according to various aspects of the present invention is configured to test a set of components and generate test data for the components. A diagnostic system automatically analyzes the test data to identify a characteristic of a component fabrication process by recognizing a pattern in the test data and classifying the pattern using a neural network.

The present invention discloses an industrial equipment operation, maintenance and optimization method and system based on a complex network model. The method includes the following steps: obtaining data of all sensors of industrial equipment, and calculating a Spearman correlation coefficient between data of every two of the sensors within the same time period; using each sensor as a node, and using the Spearman correlation coefficient as a weight of a network edge, to construct a fully connected weighted network; and obtaining, when an adjustment instruction for a target feature is received, a currently optimal parameter adjustment path of the target feature based on the fully connected weighted network. In the present invention, production equipment in reality is digitized to construct a complex network oriented to industrial big data. An optimal path for equipment parameter tuning may be found by using the network, thereby reducing dependence of an enterprise on a domain expert.

A device management system includes a device information transmitting apparatus that collects information about a device, and a management apparatus that communicates with the device information transmitting apparatus via a communication network. At least one of the management apparatus and the device information transmitting apparatus includes an anomaly state input unit that receives an anomaly state of the device, and a designating unit that designates, in accordance with the received anomaly state, first information to be used to identify a cause of the anomaly state of the device. The device information transmitting apparatus includes a first information transmitting unit that transmits the first information to the management apparatus.

Method for assessing health conditions of industrial equipment (S), said equipment having one or more determined failure modes (F.sub.1, . . . , F.sub.N), each of said failure modes having one or more determined failure causes (FC.sub.D1, . . . , FC.sub.DK) and/or one or more undetermined failure causes (FC.sub.U1, . . . , FC.sub.UM), further comprising: acquiring input data (D.sub.IN) related to said equipment; calculating failure mode assessment data (R.sub.Fi, RUL.sub.Fi, A.sub.Fi, RSK.sub.Fi, POF.sub.Fi) for each failure mode (F.sub.i) determined for said equipment, wherein the calculation of said failure mode assessment data comprises: if said failure mode (F.sub.i) has one or more determined failure causes (FC.sub.D1, . . . , FC.sub.DK): executing a first calculation procedure to calculate failure cause assessment data (R.sub.FCj, RUL.sub.FCj, A.sub.FCj) for each failure cause (FC.sub.Dj) determined for said failure mode, said failure cause assessment data being calculated on the basis of said input data (D.sub.IN); calculating said failure mode assessment data on the basis of the failure cause assessment data (R.sub.FCj, RUL.sub.FCj, A.sub.FCj) calculated for each failure cause (FC.sub.Dj) determined for said failure mode; if said failure mode (F.sub.i) has one or more undetermined failure causes (FC.sub.U1, . . . , FC.sub.UM), executing a second calculation procedure to calculate said failure mode assessment data, said failure mode assessment data being calculated on the basis of said input data (D.sub.IN); calculating a system assessment data (R.sub.S, RUL.sub.S, A.sub.S, RSK.sub.S, POF.sub.S) for said equipment, said system assessment data being calculated on the basis of said failure mode assessment data (R.sub.Fi, RUL.sub.Fi, A.sub.Fi, RSK.sub.Fi, POF.sub.Fi).

Embodiments of the present disclosure include a method for controlling a power generation system, the method including: detecting a gauge measurement of an operating parameter while visually monitoring a gauge of the power generation system during operation of the power generation system; calculating an expected value of the operating parameter based on a library of modeling data for the power generation system; calculating whether a difference between the gauge measurement of the operating parameter and the calculated expected value of the operating parameter exceeds a predetermined threshold; and adjusting the power generation system in response to the difference exceeding the predetermined threshold, wherein the adjusting includes one of calibrating the gauge or modifying an operating setting of the power generation system.

Systems and methods of monitoring solidification quality and automatic correcting any detected defect in additive manufacturing are described. The present disclosure includes a build station for manufacturing one or more parts and a controller having one or more computer-vision based system coupled to the build station. One or more camera is provided to obtain a plurality of images of the solidified parts at predetermined settings. The present disclosure introduces a predictive model trained by machine learning algorithm, the predictive model calculates level of solidification quality of a manufactured part and build parameters value to be adjusted. The present disclosure introduces a plurality of validation coupons having various shapes to enhance more accuracy in manufacturing, wherein the validation coupons further include block data which is distributed to electronic ledger system.

A display unit of a design assistance system highlights an error section of a 3D model and displays at least one of a designation condition recognized by an elimination condition recognition unit and an elimination section.