The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manufacturer by comparing the results with the data of the on-board monitoring system installed on the machine. Novel aspects of our approach include the detection of the pre-boring stage, which has an intermediate amplitude that masks the real drilling cycles, and the use of the percussion instantaneous frequency, which is estimated by acoustic recordings.
This paper deals with the application of Echo State Network (ESN) model to robust fault diagnosis of the Twin Rotor Aero-Dynamical System (TRAS) through modeling the uncertainty of the neural model with the so-called Model Error Modeling method (MEM). The work describes the modeling process of the plant and scenarios in which the system is under influence of the unknown fault. In such fault scenarios the ESN model together with MEM are used to form the uncertainty bands. If the system output exceeds the uncertain region the fault occurrence is signalized. All data used in experiments are collected from the TRAS through the Matlab/Simulink environment.
In this paper, the actuators and sensors fault detection and localization using a system model is considered. To obtain the system model, the neural network modeling is used. The artificial feedforward neural network with dynamic neurons in the state-space representation is proposed. To estimate the neural network parameters, the Adaptive Random Search algorithm with projection is used. To identify, which of actuators or sensors is faulty, the system input estimator is proposed. The input and output residuals being the difference between the system input and output and its estimates are used to detect and isolate the faults. The final part of the paper presents an application study, which clearly confirms the effectiveness of the proposed approach.
This paper deals with the application of state space neural network model with delays to design a nonlinear model for a laboratory stand of the Two Rotor Aero-dynamical system as an example of the MIMO (multi input multi output) system. The work presented is the first part of the researches on the design of the nonlinear model predictive control and focuses on obtaining of the best system model. The work describes the methodology of system analysis to obtain the most informative data which can be successfully used in training of the neural network. The system analysis is based on spectral analysis with Fourier's transform. All data used in experiments is obtain from the realtime laboratory stand which is working in Matlab/Simulink RTW environment.
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