A method for evaluating energy consumption in an industrial plant, includes capturing sensor data from at least one level of the industrial plant, from the sensor data, digitally obtaining energy consumption curves, an energy consumption curve representing, along a certain time period, discrete values of energy consumption corresponding to time intervals Δt. The method also includes; applying a clustering algorithm for digitally computing energy consumption patterns, wherein each pattern represents a set of energy consumption curves grouped according to a similarity metric and includes discrete values of energy consumption corresponding to time intervals Δt; capturing data of the production achieved during the time period at at least one level; calculating the aggregated sum of the discrete values, thus obtaining the aggregated energy consumption for each curve; and digitally establishing a relationship between aggregated energy consumption for each energy consumption curve during the time period and said captured data of production.

At least one embodiment includes an apparatus for the real time correlation of bathroom assets for maintenance requirements. The apparatus includes at least one flow sensor configured to generate water usage data for a bathroom device, at least one presence sensor configured to generate user presence data for a room including the bathroom device, and a controller configured to analyze the usage data and the user presence data to generate a maintenance message in response to the analysis.

An estimating device includes a storage unit that stores in association with each other state history information showing, as a history, a state of electrical equipment in which an abnormality has occurred, and cause history information showing, as a history, a cause of the abnormality, an acquisition unit that acquires state information indicating the state of the electrical equipment in which the abnormality has occurred, a detection unit that detects, from among a plurality of the state history information stored in the storage unit, at least one of the state history information that is similar to the state information acquired by the acquisition unit, and a notification unit that issues a notification concerning the cause of the abnormality, on the basis of the cause history information that is associated with the state history information detected by the detection unit.

Examples of a diagnostic tool include a LAN port, a human-machine interface (HMI), and a CPU configured to transmit a command issued from the HMI by a user to a connection destination of the LAN port, wherein the diagnostic tool is configured to be incapable of wireless communication.

A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

A collector (110) included in a data collection apparatus (100) performs data collection to collect data from a PLC (604, 605). A controller (150) included in the data collection apparatus (100) determines whether the collector (110) is valid depending on whether the collector (110) at a time when an instruction to start the data collection is provided matches the collector (110) at a preset time, and causes the collector (110) to start the data collection in response to the collector (110) being valid.

A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

Systems and methods related to communication with and control of network-enabled water devices and sensors of various water systems are disclosed. Such water systems may include water filtration systems, water reclamation systems, sump pump systems, pool or spa systems, water softening systems, and plumbing systems. Such water devices may include chemical controllers, smart valves, pool pumps, sump pumps, water softeners, residential appliances, and manifolds. Such sensor devices may include flow meters, splash detectors, motion sensors, moisture sensors, humidity sensors, chemical sensors, water level sensors, pressure sensors, and cameras. Data received from network-enabled water devices and sensors may be processed at a remote server or a local controller, which may cause corresponding alerts or maintenance requests to be sent to one or more user devices or service providers or may automatically control one or more of the water devices and sensors based on analysis of the data.

Systems and methods for monitoring pharmaceutical manufacturing processes are described and disclosed herein. The systems and methods monitors and creates a historic record of the pharmaceutical manufacturing process and provides it to an end user or a plurality of end users. Consequently, the methods provide a means to perform quality control on an integrated level whereby a pharmaceutical manufacturer can ensure data and product integrity and minimize cost.

A method and system for monitoring a rail vehicle are provided. The method includes: a local device acquires current operating data and historical operating data of a rail vehicle from a controller of the rail vehicle, herein the local device, which is connected with the controller, is a portable testing device; the local device obtains fault information according to the current operating data and the historical operating data, herein the fault information includes at least one of a fault code and a fault description; the local device obtains a maintenance plan corresponding to the fault information, herein the maintenance plan is obtained through a remote device or a server side; and the local device maintains the rail vehicle according to the maintenance plan. The disclosure solves a technical problem in the traditional art that a troubleshooting process of a rail vehicle is difficult, and when the rail vehicle has a fault, the fault cannot be solved timely.