Approaches are provided for an apparatus having an interface with an input. The input is configured to receive a plurality of current alerts. The apparatus further includes a display device configured to display the current alerts, and a memory configured to store a prior case data structure comprising prior evidence alerts. The apparatus further includes a processor configured to access the prior case data structure from the memory. The processor is further configured to determine whether the prior evidence alerts include a first prior evidence alert related to the first current alert and a second prior evidence alert related to the second current alert. In response to determining the prior evidence alerts include the first and second prior evidence alerts, the processor is further configured to display at the display device an indication of a prior relationship between the first current alert and the second current alert.

A malfunction estimation method allows personnel and spare parts to be provided according to the nature of the fault by means of the estimation of malfunction using machine noise analysis. The sounds of the machines are recorded online or offline via a mobile application on the mobile devices of operators or customers. Mentioned sounds and sounds from an audio database are analyzed by fault classification algorithms and the fault information is reported to the operator or customer online or offline via mobile application or an internet platform. It is ensured that the malfunctioned part is detected with only one mobile device without the need for additional equipment.

The present invention relates to a method and a system for production accounting in process industries using Artificial Intelligence (AI). More particularly the present invention relates to fault detection in a plurality of measuring instruments and process equipment in a process plant. A plurality of measured signals from the measuring instruments is received by the process control system and noise is extracted from the plurality of measured signals. The extracted noise is correlated with a noise extracted from a plurality of reference signals using an AI based data analysis technique. Further, the process control system identifies deviations in the one or more parameters. The process control system detects the faults the plurality of measuring instruments or the process equipment using the correlated noises and the identified deviations of the one or more parameters.

A sound source estimation system includes a microphone that detects sound which is generated from an object including a rotary device, a rotation speed acquiring unit that acquires a rotation speed of the rotary device, a frequency analyzing unit that generates frequency sound data indicating change of a frequency spectrum of sound detected by the microphone, a degree calculating unit that calculates a degree based on a loudest sound frequency indicating loudest sound out of frequencies of the sound and the rotation speed, and a degree comparing unit that determines a component with a degree closest to the degree as a sound source candidate of the noise.

Systems, methods, and computer program products for monitoring a health condition of a tool. Operational data is collected from a machine while the machine is operating in a predetermined manner with the tool in each of at least two known health conditions. A plurality of features is extracted from the operational data, a training dataset is generated from the extracted features, and an analytic model is trained using the training dataset. The analytic model can then be used to determine the health condition of the tool by providing features extracted from operational data received from one or more field machines to the analytic model. The analytic model may then determine a health condition of the tool in the field machine based on like features extracted from the operational data from the one or more field machines.

One embodiment of the present invention provides an apparatus, or the like, which detects an anomaly of a controller of a control system by learning relationship between input and output of the controller. An anomaly detection apparatus which is one embodiment of the present invention includes a first acquirer, a second acquirer, a history recorder, an estimator, and a first anomaly determiner. The first acquirer acquires an input signal to a control apparatus which executes control on a controlled apparatus. The second acquirer acquires an output signal from the control apparatus. The history recorder records information regarding the acquired input signal and the acquired output signal as history. The estimator estimates the output signal using the history and an estimation model. The first anomaly determiner determines an anomaly of the control apparatus by comparing the estimated output signal with the acquired output signal.

The present disclosure relates to an apparatus for diagnosing a state of a photovoltaic device, a building Integrated Photovoltaics (BIPV) device, etc., and more particularly to an apparatus for diagnosing photovoltaic power generation, which diagnoses a state of the specific photovoltaic device by comparing the difference in power generation between grouped photovoltaic devices through analysis of power generation for the same period in the past through machine learning, etc., wherein the apparatus processes power generation information, which is collected from the photovoltaic devices, based on failure history and maintenance and repair information of each photovoltaic device and performs precise grouping by minimizing error information regarding a power generation trend based on information such as regional weather information and environment information for a region where each photovoltaic device is located, so that a power generation trend can be analyzed with improved accuracy of analysis of state.

A computer implemented method includes turning off a sensor, receiving fall curve data from the sensor, and comparing the received fall curve data to a set of fall curve signatures to identify the sensor or a sensor fault.

A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations. The analytics engine also can determine when there is a deviation or potential of deviation from desired system performance by an industrial asset or employee, and provide a notification, a recommendation, or an instruction to rectify or avoid the deviation.

A method for calculating a remaining useful life of an apparatus comprises the following steps. Time-series of previous runs and a current run of the apparatus are provided containing data of sensors configured to monitor parameters of the apparatus. An error occurs when a parameter breaches a threshold. Cumulative counts of errors occurring during a run are calculated. A linearly decreasing remaining useful life is calculated for previous runs. Breakdowns of the apparatus are mapped in an error space. Each dimension of the error space refers to one type of an error. The breakdown points are mapped at coordinates which represent cumulative error counts at the time of the breakdowns. A test point representing cumulative error counts of the current run is mapped. At least two nearest breakdown points to the test point are identified. The remaining useful life of the apparatus is calculated based on the nearest breakdown points.