Various embodiments include apparatus and methods to estimate properties of rock, drill bit, or a combination thereof associated with a drilling operation. The properties can include, but are not limited to, rock chip size, drill bit dullness, drilling efficiency, or a combination selected from rock chip size, drill bit dullness, and drilling efficiency. The estimate may be accomplished from correlating detected acoustic emission with detected electromagnetic emissions. In various embodiments, formation brittleness may be determined. The various estimates may be used to direct a drilling operation. Additional apparatus, systems, and methods are disclosed.

The invention relates to a wear component of a milling machine, to a milling machine equipped with such a wear component, and to a method for determining the wear on a wear component. The wear component has associated with it at least one contactlessly readable electronic component for determining the wear on the wear component. Provision is made according to the present invention that at least one sensor is connected to at least one contactlessly readable electronic component for the transfer of data; that the contactlessly readable electronic component is embodied to receive measured data of the sensor and furnish them for contactless reading; and that at least one measurement portion of the sensor is guided, along at least one wear direction to be monitored, into a wear region or along the wear region of the wear component. The invention makes possible a better milling result as a result of optimized maintenance.

A system for monitoring a bit condition in a drilling operation comprises a processor unit and a non-transitory computer-readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining signals representative of at least vibrations of a drilling mast having at least one bit during a drilling operation; interpreting the signals into vibration values; continuously monitoring the vibration values in real time; and assessing and outputting a condition of the bit as a function of the continuous monitoring of the vibration values.

A system for monitoring a bit condition in a drilling operation comprises a processor unit and a non-transitory computer-readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining signals representative of at least vibrations of a drilling mast having at least one bit during a drilling operation; interpreting the signals into vibration values; continuously monitoring the vibration values in real time; and assessing and outputting a condition of the bit as a function of the continuous monitoring of the vibration values.

A wear indicator is provided for a wear member of a machine wherein the wear member is subject to wear during use of the machine. The wear indicator may include a plug member configured to be positioned in a region of the wear member subject to wear. The plug member may be oriented with a central axis extending in a direction substantially parallel to a direction of wear of the wear member. The wear indicator may include a plurality of perceptibly different and distinct axial cross sections taken perpendicular to the central axis of the plug member in axially spaced planes along the central axis.

A testing device that includes a wear testing device, a sensor array, and a controller. The wear testing device includes a sample rotation element configured to hold and to rotate a sample; and a cutting element holder configured to hold a cutting element and to engage the cutting element with the sample as the sample rotates. The sensor array includes an acoustic emissions (AE) sensor array comprising a plurality of AE sensors, the plurality of AE sensors configured to measure a plurality of acoustic signals generated during engagement between the cutting element and the sample; and a load sensor. The controller is communicably connected to the sensor array and configured to determine a toughness and a wear resistance of the cutting element using the plurality of acoustic signals, the applied load, and a wear state of the cutting element.

Methods and systems for well tool wear classification system are provided. A wear classifier tool is configured to classify wear of a scanned well tool using a machine learning engine. Computer-readable memory stores a training dataset and a trained ML model. The training data set includes scanned image data and associated labels representative of classification types of failure. The trained ML model has a neural network. The wear classifier tool can output data identifying a failure mode of the scanned well tool based on classification of input by the machine learning engine. A database is configured to stored historical data on scanner type, patterns of scanner cutting elements, sensor type, and age and usage conditions. A scanning system includes a camera and a three-dimensional (3D) scanner configured to scan a drill bit.

Methods and systems for well tool wear classification system are provided. A wear classifier tool is configured to classify wear of a scanned well tool using a machine learning engine. Computer-readable memory stores a training dataset and a trained ML model. The training data set includes scanned image data and associated labels representative of classification types of failure. The trained ML model has a neural network. The wear classifier tool can output data identifying a failure mode of the scanned well tool based on classification of input by the machine learning engine. A database is configured to stored historical data on scanner type, patterns of scanner cutting elements, sensor type, and age and usage conditions. A scanning system includes a camera and a three-dimensional (3D) scanner configured to scan a drill bit.

The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.