An apparatus comprises an electronic equipment chassis comprising a housing and at least one lid, the housing comprising a control panel with a first set of one or more status indicators. The apparatus also comprises at least one latch configured for securing the at least one lid to the housing, the at least one latch comprising a second set of one or more status indicators. The apparatus further comprises a processing device configured to determine status information for electronic equipment housed in the electronic equipment chassis, the status information characterizing whether at least one of opening and removing the at least one lid is safe to perform at a given time, and controlling, based at least in part on the determined status information, at least one of the first set of indicators and at least one of the second set of indicators.

A substrate processing system includes: an acquiring unit configured to acquire process data of each step when each step included in a predetermined process is executed under different control conditions; an extracting unit configured to divide each step into a first section in which the process data fluctuates and a second section in which the process data is converged, and extract first data belonging to the first section and second data belonging to the second section from the process data; and a monitoring unit configured to monitor the process data by comparing one or both of an evaluation value that evaluates the first data and an evaluation value that evaluates the second data with corresponding upper and lower limit values.

A controller diagnostic method includes transmitting a Diagnostic Trouble Code (DTC) request signal to a plurality of controllers; receiving a first frame of the plurality of controllers in response to the DTC request signal; delaying a transmission time of a flow control signal and transmitting the delayed flow control signal to the plurality of controllers; and receiving a DTC information by at least one consecutive frame provided by the plurality of controllers in response to the delayed flow control signal.

An integrated vehicle health management (IVHM) system to resolve equipment-fault related anomalies detected by cyber intrusion detection system (IDS). A benefit of the present system is that it can result in fewer alerts that need manual analysis. A combination of cyber and monitoring with integrated vehicle health management (IVHM) may be a high value differentiator. As a solution gets more mature through a learning loop, it may be customized for different customers in a cost-effective manner, something that might be expensive to develop on their own for most original equipment manufacturers (OEMs). An IVHM symptom pattern recognition matrix may link a pattern of reported symptoms to known equipment failures. This matrix may be initialized from the vehicle design data but its entries may get updated by a learning loop that improves a correlation by incorporating results of investigations.

A method for learning and detecting an abnormal part of a device through artificial intelligence comprises: an information collection step for collecting a current waveform of a current value that changes over time in a driving state of at least one device and collecting information about a faulty part of the device, together with current waveform information before a fault occurs in the device; a model setting step for learning, by a control unit, information collected in the information collection step and setting a reference model of a current waveform for each faulty part of the device; and a detection step for, when an abnormal symptom of the device is detected in a real-time driving state, comparing, by the control unit, a real-time current waveform of the device and the reference model, and detecting and providing an abnormal part regarding the abnormal symptom of the device.

The present disclosure relates to systems and methods for determining abnormal information associated with a vehicle. The systems may perform the methods to obtain real-time information associated with a bicycle and obtain reference information associated with the bicycle. The systems may also perform the methods to determine, based on the real-time information and the reference information, abnormal information associated with the bicycle, and transmit the abnormal information associated with the bicycle.

By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models). Stateful models and their variations may be more suitable for generating near perfect predictions when they are trained on a large enough dataset, while hybrid models may be more suitable for generalizing better given smaller datasets with changing conditions.

A computer-implemented method for controlling and/or monitoring a production plant (110) is proposed. The production plant (110) comprises at least one process chain (112) comprising at least one batch process (114). The method comprises the following steps: a) at least one step of determining of input data (132), wherein the input data comprises at least one quality criterion and production plant layout data, wherein the step comprises retrieving the production plant layout data and receiving information relating to the quality criterion via at least one communication interface (158); b) at least one prediction step (134), wherein in the prediction step operating conditions for operating the production plant (110) are determined by applying at least one trained model (136) on the input data, wherein the trained model (136) is at least partially data-driven by being trained on sensor data from historical production runs; c) at least one control and/or monitoring step (140), wherein the operating conditions are provided.

An equipment state monitoring device includes: a feature amount extracting unit to extract a feature amount of operation data in which a state of equipment is measured; an operation pattern determining unit to determine whether an operation pattern of the equipment when the operation data is measured is a learned pattern in which a determination range of a state of the equipment is learned or an unlearned pattern; a feature amount correcting unit to correct the feature amount of the operation data corresponding to the operation pattern determined as the unlearned pattern to correspond to the learned pattern on a basis of a relationship between an operation pattern of the equipment and a feature amount of operation data; and an equipment state determining unit to determine a state of the equipment on a basis of the corrected feature amount and a determination range of a state of the equipment.

A method of detecting an operational anomaly of an industrial system can include receiving operational values for a plurality of process parameters from an industrial system at a localized anomaly detection system, wherein the plurality of process parameters, accessing a machine learning model stored in a non-volatile memory system operating within the localized anomaly detection system, to determine predicted values for the process parameters based on the operational values of the process parameters received from the industrial system, and determining residual values for the process parameters, each representing a difference between a respective one of the predicted values and a respective one of the operational values.