Methods and systems for the industrial internet of things

Abstract

The system generally includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. The multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. Unassigned outputs are configured to be switched off producing a high-impedance state. The local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history of a data acquisition unit in which the onboard card set is located. The local data collection system is configured to manage data collection bands.

What is claimed is: 
     
         1 . A system for data collection, processing, and utilization of signals from a machine in an industrial environment, the system comprising:
 a local data collection system having at least one sensor signal obtained from the machine in the industrial environment;   a platform including a computing environment connected to the local data collection system; and   a sensor in the local data collection system configured to be connected to the machine for obtaining the at least one sensor signal from the machine;   wherein the local data collection system includes at least one input connected to the sensor and at least one output;   wherein the local data collection system is configured to record gap-free digital waveform data from the at least one input; and   wherein the local data collection system includes a neural net expert system configured to provide intelligent management of data collection bands from the recorded gap-free digital waveform data.   
     
     
         2 . The system of  claim 1 , wherein the local data collection system includes multiple inputs and multiple outputs having the at least one input connected to the sensor and another input connected to another sensor;
 wherein the multiple outputs include the at least one output and another output that are configured to be selected between at least one of:
 a condition in which the at least one output is configured to be selected between delivery of the at least one sensor signal and another sensor signal; or 
 a condition in which there is simultaneous delivery of the at least one sensor signal from the at least one output and the other sensor signal from the other output; and 
   wherein each of the multiple inputs is configured to be individually assigned to any of the multiple outputs; and   wherein the local data collection system is configured to record the gap-free digital waveform data simultaneously from the at least one input and the other input.   
     
     
         3 . The system of  claim 1 , wherein the neural net expert system includes machine learning that uses at least one neural network to provide the intelligent management of the data collection bands. 
     
     
         4 . The system of  claim 1 , wherein the neural net expert system includes machine learning that is configured to adjust at least one of weights, structures, or rules based on feedback that includes at least one or more inputs or measures of success for training or improving an initial model. 
     
     
         5 . The system of  claim 1 , wherein the neural net expert system is configured to define smart bands from the data collection bands such that the neural net expert system is further configured to pair the smart bands with at least one neural network for providing diagnosis of the machine, and wherein the smart bands refer to at least one of: a specific frequency band or a group of spectral peaks. 
     
     
         6 . The system of  claim 5 , wherein the group of spectral peaks includes signal attributes that further include at least one of: harmonics of a single peak, a true-peak level, a crest factor derived from a time waveform, an overall derivation from a vibration envelope spectrum, or a logical combination of the signal attributes. 
     
     
         7 . The system of  claim 1 , wherein the neural net expert system is configured to define smart bands from the data collection bands, wherein the neural net expert system uses the smart bands in a neural approach that utilizes weighted triggering of multiple input stimuli into a group of neurons which feed a weighted output to other groups of neurons, and wherein the output of the group of neurons is classified as the smart bands which feed the other groups of neurons. 
     
     
         8 . The system of  claim 1 , wherein the local data collection system is configured to create data acquisition routes based on hierarchical templates that each include the data collection bands related to the machine associated with the data acquisition routes. 
     
     
         9 . The system of  claim 8 , wherein at least one hierarchical template of the hierarchical templates is associated with similar elements associated with the machine and another machine, and wherein at least one hierarchical template of the hierarchical templates is associated with the machine being proximate in location to the other machine. 
     
     
         10 . The system of  claim 1 , wherein the local data collection system includes a graphical user interface system configured to use the neural net expert system to manage the data collection bands for providing diagnosis of the machine. 
     
     
         11 . The system of  claim 1 , wherein the platform is configured to determine a change in relative phase based on the recorded gap-free digital waveform data. 
     
     
         12 . The system of  claim 11 , wherein the platform is configured to determine an operating deflection shape based on the change in relative phase and the recorded gap-free digital waveform data. 
     
     
         13 . The system of  claim 1 , wherein the local data collection system is configured to obtain the recorded gap-free digital waveform data from the machine while the machine and another machine are both in operation, and wherein the local data collection system is configured to characterize a contribution from the machine and the other machine in the recorded gap-free digital waveform data. 
     
     
         14 . The system of  claim 1 , wherein the gap-free digital waveform data is obtained simultaneously from each sensor with maximum resolvable frequencies sufficiently large to capture periodic and transient impact events. 
     
     
         15 . A computer implemented method for data collection, processing, and utilization of signals from a machine in an industrial environment, the method comprising:
 connecting a sensor of a local data collection system to the machine in the industrial environment;   obtaining at least one sensor signal from the machine;   recording gap-free digital waveform data from at least one input of the sensor connected to the machine; and   a neural net expert system providing intelligent management of data collection bands from the recorded gap-free digital waveform data.   
     
     
         16 . The method of  claim 15 , wherein the neural net expert system includes machine learning that uses at least one neural network to provide the intelligent management of the data collection bands. 
     
     
         17 . The method of  claim 15 , wherein the providing intelligent management further comprises defining smart bands from the data collection bands and pairing the smart bands with at least one neural network for providing diagnosis of the machine, and wherein the smart bands refer to at least one of: a specific frequency band or a group of spectral peaks. 
     
     
         18 . The method of  claim 15 , wherein the providing intelligent management further comprises defining smart bands from the data collection bands and using the smart bands in a neural approach that utilizes weighted triggering of multiple input stimuli into a group of neurons which feed a weighted output to other groups of neurons, and wherein the output of the group of neurons is classified as the smart bands which feed the other groups of neurons. 
     
     
         19 . The method of  claim 15 , further comprising:
 obtaining another sensor signal from the machine in the industrial environment separate and distinct from the at least one sensor signal;   selecting a first output and a second output of multiple outputs between at least one of:
 a condition in which the first output is configured to be selected between delivery of the at least one sensor signal and the other sensor signal; or 
 a condition in which there is simultaneous delivery of the at least one sensor signal from the first output and the other sensor signal from the second output; and 
   individually assigning one or more inputs to any of the multiple outputs;   wherein the recording gap-free digital waveform data is provided simultaneously from the at least one input and another input.   
     
     
         20 . A non-transitory computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising:
 connecting a sensor of a local data collection system to a machine in an industrial environment;   obtaining at least one sensor signal from the machine;   recording gap-free digital waveform data from at least one input of the sensor connected to the machine; and   a neural net expert system providing intelligent management of data collection bands from the recorded gap-free digital waveform data.   
     
     
         21 . The computer readable storage medium of  claim 20 , wherein the neural net expert system includes machine learning that uses at least one neural network to provide the intelligent management of the data collection bands. 
     
     
         22 . The computer readable storage medium of  claim 20 , wherein the operations performed by the one or more processors further comprise obtaining the recorded gap-free digital waveform data from the machine while the machine and another machine are both in operation, and wherein the local data collection system is configured to characterize a contribution from the machine and the other machine in the recorded gap-free digital waveform data.