Methods and systems for a data marketplace in a conveyor environment includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding conveyor in an industrial environment, wherein the at least one data collector is structured to collect detection values from at least one sensor of a power roller of the at least one corresponding conveyor; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.

Monitoring of a machine is performed by an acoustic monitor which acquires, through an acoustic sensor, acoustic signals from a vicinity of a machine, while the machine is operative. A processor calculates a frequency spectrum of a segment of the acquired acoustic signals, determines boundaries of a frequency band to be analyzed and extracts, from the calculated frequency spectrum, a base frequency window in the determined boundaries, and one or more harmonics windows of harmonics of the determined boundaries. For each of the base and harmonic windows a weight based on a distribution of values of frequencies in the windows is determined and a parameter of operation of the machine is calculated as a function of a weighted sum of the base and harmonic windows. The operation of the machine is evaluated responsive to the calculated parameter.

Systems and methods for data collection and processing are described, including a plurality of variable groups of industrial sensor inputs operationally coupled to an industrial environment and a multiplexer communicatively coupled to the industrial sensor inputs; and a controller configured to receive and monitor the data and adaptively schedule the data collector.

Systems, apparatus, and program code, and methods for monitoring the health and other conditions of the valve, are provided. An exemplary system for monitoring the condition of the gate valve includes a logic module configured to perform the operations of receiving sensor data providing an acoustic emission, vibration, and/or stream level signature and determining the level of lubricity, level of friction, level of surface degradation, and leakage rate at a gate-valve seat interface. An exemplary method for monitoring the condition of the gate valve includes receiving sensor data providing an acoustic emission, vibration, and/or stream level signature and determining the level of lubricity, level of friction, level of surface degradation, and leakage rate at a gate-valve seat interface.

A method for detecting defects in working CNC tools in real time, implemented in an electronic device, includes acquiring sounds of operation of a tool during a cutting or other operation process and dividing the acquired cutting sounds into a plurality of recordings of audio according to a preset time interval. Time-frequency features of the plurality of recordings of audio are acquired according to multiple feature transformation methods and a fusion feature image of the cutting sound is formed according to the extracted time-frequency features. A tool detection model is generated by training the fusion feature image, and any defects of the tool and any defect types the tool has are detected according to the tool detection model.

An example method includes determining an expected sound profile corresponding to a given task for a robotic device. The method further includes detecting a sound profile during execution of the given task by the robotic device. The method also includes determining one or more differences in amplitude for at least one frequency range between the detected sound profile and the expected sound profile corresponding to the given task for the robotic device. In response to determining the one or more differences in amplitude for the at least one frequency range between the detected sound profile and the expected sound profile, the method additionally includes identifying at least one component of the robotic device associated with the detected sound profile during execution of the given task. The method further includes adjusting control data for the at least one component of the robotic device.

A CNC machine shaping tool is efficiently used by monitoring human audible sound during shaping. A sound information set is created for a tool shaping a workpiece. Shaping sounds are recorded and sliced into short term units. A human operator assigns tool condition labels to each slice. Short term units are combined into mid term units. Noise is reduced by profiling. Mid term sound related features of time and frequency domains are extracted. Dimensionality is reduced by robust principal component analysis. The principal component set is balanced, e.g. by SMOTE. A classifier and principal components are selected. An information set of patterns of values of selected principal components for the tool is created. In an industrial setting, shaping sounds are recorded, noise reduced and select principal component vector values are compared to the tool condition labeled patterns of values in the information set to identify tool condition before deterioration.

The present disclosure describes systems and methods for data collection in an industrial environment having self-organization functionality. A method can include analyzing a plurality of sensor inputs, sampling data received from the sensor inputs, and self-organizing at least one of (i) a storage operation of the data, (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs. The method may further include receiving instructions directing a mobile data collector unit to operate sensors at a target, transmitting a communication to one or more other mobile data collector units regarding the instructions, and self-organizing a distribution of the mobile data collector unit.

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.

Systems and methods for data collection and detection of noise patterns. A system may include a data collector communicatively coupled to a plurality of input channels, wherein at least one of the plurality of input channels is operatively coupled to a vibration detection facility structured to detect a noise pattern of an industrial machine, a library to store the detected noise pattern, an interface circuit structured to make the noise pattern available to a noise pattern marketplace, the noise pattern marketplace including a plurality of noise patterns from a plurality of industrial machines, and a user interface for accessing the plurality of noise patterns of the noise pattern marketplace.