A method for modelling technical systems having a plurality of technical components, including the step of assigning a component Markov chain to each component having a Markov chain for representing various states of the respective component, at least one input one failure mode for externally triggering a transition from one state of the Markov chain into another state of the Markov chain, and at least one output failure mode to each Markov chain for propagating failures to other components, is provided.

A control device includes: a feature extraction unit that calculates one or more feature amounts from one or more state values; a processing unit that calculates a score based on the one or plurality of feature amounts calculated by the feature extraction unit with reference to a learning model; a determination unit that generates a determination result indicating whether any abnormality has occurred in a monitoring target based on the score; a first data storage unit that stores at least one of data related to processing in the feature extraction unit and data related to processing in the processing unit; a second data storage unit that stores an arbitrary state value capable of being referred to by the control device; and an authority management unit that restricts access to the first data storage unit.

The invention discloses a method for detecting service performance of a tunnel lining based on defect characteristics of the tunnel lining. A tunnel, an external load and stratum conditions are simulated by establishing a model using a model test method. A structural stress failure test for the model is carried out, and test results of the defect characteristics of a simulation lining of the model are recorded. A corresponding relationship between the defect characteristics of the simulation lining and the remaining bearing capacity interval is established according to the recorded test results. Detection results of defect characteristics of the tunnel lining are recorded using an in-situ detection method, and a remaining bearing capacity interval of the tunnel lining is determined based on the detection results according to the corresponding relationship between the defect characteristics of the simulation lining and the remaining bearing capacity interval of the model.

A method for monitoring a condition of a sliding bearing operated with lubricating oil for a rotating component includes calculating, by a control unit as an output variable of a sliding bearing model, a calculated value of a minimum gap thickness of the sliding bearing. The calculated value is calculated by orbit analysis from at least one physical sliding bearing model to which at least a rotational speed of the rotating component, a bearing load, and a temperature of the sliding bearing are supplied as input variables. The method further includes measuring, with at least one sensor, a minimum gap thickness to provide a measured value of the minimum gap thickness, and comparing the measured value of the minimum gap thickness with the calculated value of the minimum gap thickness for the purpose of adjustment.

The present disclosure discloses a estimation methods of actuator faults based on bayesian learning, and belongs to the technical field of system detection. According to the present disclosure, an actuator fault is modeled based on a random walking model, and a joint posterior probability distribution of a system state variable and the actuator fault is represented using two mutually independent hypothesis distributions based on a variational Bayesian theory; a system state variable and an actuator fault of a system at a moment k are predicted at a moment k−1; and a predicted system state variable and a predicted actuator fault are iteratively updated at the moment k according to the Bayesian theory to output an estimated value of the system state variable at the moment k as well as a variance of the estimated value and an estimated value of the actuator fault at the moment k as well as a variance of the estimated value. In the present disclosure, by fully using a structure that Bayesian learning is applied to online estimation and decoupling the system state variable of mutually coupled variables and the actuator fault, an actuator fault estimation method for a random system is provided, and can estimate an actuator fault of the random system.

Described is determining defects of an object produced using an additive manufacturing process, including: determining spatially resolved first data relating to n objects, the first data defines a process coordinate system for each of the n objects, determining measurement data relating to the n objects by imaging the n objects, the measurement data defines, for each of the n objects, an object representation in a measurement coordinate system, determining which coordinates of at least one section of the measurement coordinate system are defect coordinates assigned to a defect in the object representation; correlating the at least one section with a corresponding section the process coordinate system in order to collect training data, training an adaptive algorithm for determining defect coordinates in spatially resolved data, by means of the training data, determining spatially resolved second data and analysing the second data for defects by means of the adaptive algorithm.

In order to provide a method for predicting process deviations in an industrial-method plant, for example a painting plant, by means of which process deviations are predictable simply and reliably, it is proposed according to the invention that the method should comprise the following: automatic generation of a prediction model; prediction of process deviations during operation of the industrial-method plant, using the prediction model.

Disclosed herein is a computing system for implementing a physics-based model, including a physics-based model configured to describe a physical complex system and perform deductive inference, and at least one processor. The at least one processor is configured to receive first data obtained such that the state information of first nodes is included therein in association with at least one of the passage of time and spatial distribution, to identify the state information for each of the first nodes in the physics-based model based on the first data in association with at least one of the passage of time and spatial distribution, and to determine parameters between variables in the physics-based model so that the state information can be reproduced for each of the first nodes in association with at least one of the passage of time and spatial distribution.

A hot water supply device including an inlet pipe, an outlet pipe, a burner unit, a heat exchanger, an exhaust aperture, a first temperature sensor detecting a measured exhaust temperature of the exhaust gas, a second temperature sensor detecting a water temperature of water entering the inlet pipe, and a processor. The processor is configured to obtain an error between the measured exhaust temperature and an estimated exhaust temperature, and detects that scale clogging has occurred inside the heat exchange tubing based on an index which is generated using the error between the measured exhaust temperature and the estimated exhaust temperature. The estimated exhaust temperature is a first predetermined value that is determined using a numerical equation which has at least the water temperature of water entering the inlet pipe and a scale number of the hot water supply device as variables of the numerical equation.

A system for use with an electric machine is provided. The system includes a processor and a memory comprising a set of memory modules, which, when executed by the processor, cause the processor to perform certain operations. The operations include receiving operational data from the electric machine, and generating, based on the operational data, a first set of diagnostic data, by executing a first memory module from the set of memory modules. The operations further include generating, based on the operational data, a second set of diagnostic data, by executing a second memory module from the set of memory modules, the second memory module including a set of parameters associated with a diagnostics model of the electric machine. Furthermore, the operations include effecting, based on the operational data, the first set of diagnostic data, and the second set of diagnostic data, a change in at least one parameter.