scholarly journals Drill bit state-oriented drilling process classification with time-series data for wheeled drilling rigs

2021 ◽  
Vol 942 (1) ◽  
pp. 012010
Author(s):  
Bartłomiej Ziętek ◽  
Jacek Wodecki ◽  
Anna Michalak ◽  
Pawel Śliwiński
Keyword(s):  
Time Series Data ◽  
Pressure Level ◽  
Series Data ◽  
Measurement Unit ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drill Bit ◽  
Feed Pressure ◽  
Drilling Rigs ◽  
Process Classification

Abstract This paper represents an analysis of the wheeled drilling rig’s drilling process. Thanks to data from the onboard measurement unit of the machine, the characteristics of the drilling process regarding state of the drill bit are identified and calculated. The aim of the work is to provide a comparison between different drill qualities and process classification using Threshold-based segmentation with feed pressure levels and duration of single hole drilling. Second methodology is hierarchical clustering to create cluster analysis. Thanks to these approaches, it is possible to detect the time when the drill bit should be changed. The obtained results state that the average drill time for a new drill bit is shorter approximately by 50% than for the worn-out bit in terms of average drilling duration. Moreover, these changes are visible in the subsystem pressure level of the machine under specific drilling regimes.

scholarly journals Coordination Pattern of the Thigh, Pelvic, and Lumbar Movements during the Gait of Patients with Hip Osteoarthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Takuya Ibara ◽  
Masaya Anan ◽  
Ryosuke Karashima ◽  
Kiyotaka Hada ◽  
Koichi Shinkoda ◽  
...  
Keyword(s):  
Time Series Data ◽  
Hip Osteoarthritis ◽  
Series Data ◽  
Measurement Unit ◽  
Control Group ◽  
Coordination Pattern ◽  
Rotational Movement ◽  
Vector Coding ◽  
Lateral Tilt ◽  
Angular Velocities

There are limited reports on segment movement and their coordination pattern during gait in patients with hip osteoarthritis. To avoid the excessive stress toward the hip and relevant joints, it is important to investigate the coordination pattern between these segment movements, focusing on the time series data. This study aimed to quantify the coordination pattern of lumbar, pelvic, and thigh movements during gait in patients with hip osteoarthritis and in a control group. An inertial measurement unit was used to measure the lumbar, pelvic, and thigh angular velocities during gait of 11 patients with hip osteoarthritis and 11 controls. The vector coding technique was applied, and the coupling angle and the appearance rate of coordination pattern in each direction were calculated and compared with the control group. Compared with the control group, with respect to the lumbar/pelvic segment movements, the patients with hip osteoarthritis spent more rates in anti-phase and lower rates in in-phase lateral tilt movement. With respect to the pelvic/thigh segment movements, the patients with hip osteoarthritis spent more rates within the proximal- and in-phases for lateral tilt movement. Furthermore, patients with osteoarthritis spent lower rates in the distal-phase for anterior/posterior tilt and rotational movement. Patients with hip osteoarthritis could not move their pelvic and thigh segments separately, which indicates the stiffness of the hip joint. The rotational movement and lateral tilt movements, especially, were limited, which is known as Duchenne limp. To maintain the gait ability, it seems important to pay attention to these directional movements.

Fault diagnosis based on feature clustering of time series data for loss and kick of drilling process

2021 ◽  
Vol 102 ◽  
pp. 24-33
Author(s):  
Zheng Zhang ◽  
Xuzhi Lai ◽  
Min Wu ◽  
Luefeng Chen ◽  
Chengda Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Data ◽  
Series Data ◽  
Drilling Process ◽  
Feature Clustering

Vibration Drilling the 0Cr18Ni9Ti Stainless Steel Micro-Hole

2012 ◽  
Vol 217-219 ◽  
pp. 1592-1595 ◽  
Author(s):  
Peng Zhang ◽  
Chang Hong Mei ◽  
Xing Yu Guo
Keyword(s):  
Stainless Steel ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drill Bit ◽  
Drilling Force ◽  
Process Mechanism ◽  
Micro Hole ◽  
Vibration Drilling ◽  
Hole Machining

Austenite 0Cr18Ni9Ti stainless steel is one of difficult-to-cut materials. It has poor dilling process, especially for micro-hole machining. The main reasons are the tiny drill, poor rigidity, easy to deviation. Moreover, the chip is difficult to discharge, so the drilling force is increased and the drill bit is easy to break, or even it is impossible for micro-hole drilling. In this paper, the vibration drilling process is adopted. The vibration drilling 0Cr18Ni9Ti stainless steel micro-hole process mechanism is researched. The stainless steel micro-hole drilling experiments are conducted. The results show that the vibration drilling can be a better solution for 0Cr18Ni9Ti stainless steel micro-hole processing.

Research on Vibration Drilling 1Cr18Ni9Ti Stainless Steel Micro-Hole

2012 ◽  
Vol 591-593 ◽  
pp. 423-427
Author(s):  
Peng Zhang ◽  
Yan Jing ◽  
Xing Yu Guo
Keyword(s):  
Stainless Steel ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drill Bit ◽  
Drilling Force ◽  
Process Mechanism ◽  
Micro Hole ◽  
Vibration Drilling ◽  
Hole Machining

The austenite 1Cr18Ni9Ti stainless steel is one of difficult-to-cut materials. It has poor dilling process, especially for micro-hole machining. The main reasons are the tiny drill, poor rigidity, easy to deviation. Moreover, the chip is difficult to discharge, so the drilling force is increased and the drill bit is easy to break, or even it is impossible for micro-hole drilling. In this paper, the vibration drilling process is adopted. The vibration drilling 1Cr18Ni9Ti stainless steel micro-hole process mechanism is researched. The stainless steel micro-hole drilling experiments are conducted. The results show that the vibration drilling can be a better solution for 1Cr18Ni9Ti stainless steel micro-hole processing.

scholarly journals Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7628
Author(s):  
Yeon-Wook Kim ◽  
Kyung-Lim Joa ◽  
Han-Young Jeong ◽  
Sangmin Lee
Keyword(s):  
Time Series Data ◽  
Learning Algorithm ◽  
Sampling Rate ◽  
Model Performance ◽  
Series Data ◽  
Measurement Unit ◽  
Clinical Test ◽  
Ensemble Model ◽  
Balance Assessment ◽  
Optimal Ensemble

In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

WEAR TESTING OF BUTTONS IN BITS FOR BLASTHOLE DRILLING

Tribologia ◽  
2018 ◽  
Vol 278 (2) ◽  
pp. 13-19 ◽  
Author(s):  
Rafał DUDEK ◽  
Krzysztof WŁADZIELCZYK
Keyword(s):  
Abrasive Wear ◽  
Blast Hole ◽  
Operating Time ◽  
Operating Conditions ◽  
Wear Testing ◽  
Hole Drilling ◽  
Drilling Process ◽  
Rock Mining ◽  
Drilling Rigs ◽  
The Impact

The article presents the results of the wear testing of buttons in selected types of bits with the diameter of 95 mm used for blast hole drilling in a rock mining. The purpose of the testing was to determine the type of the wear of peripheral and frontal buttons in the actual operating conditions of bits and the impact of selected parameters of the drilling process and of sharpening the buttons on their durability. Tests of button wear were carried out by the blasthole drilling in deposits of the Devonian and Triassic dolomites. For the blast hole drilling with tested bits, drilling rigs HSB 500 and HBM 60, equipped with down-the-hole impact mechanisms VKP 95-2 from the company Permon were used. Tests on the wear of buttons were carried out according to the adopted methodology, taking into account both their abrasive wear and wear through crushing or falling out. During the drilling of holes, every effort was made to use fixed values of parameters of the drilling process, except for the value of drill stem rotation speed, because one of objectives of the research was to determine its impact on the abrasive wear of tested bits buttons. The obtained results of tests proved that the predominant type of wear of button bits for blast hole drilling is an abrasive wear of frontal buttons, and regular sharpening of the buttons allows increasing the operating time of rock bits by up to 35%.

scholarly journals Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Hee-Un Kim ◽  
Tae-Suk Bae
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Time Series Data ◽  
Short Term Memory ◽  
Advanced Technology ◽  
Series Data ◽  
Measurement Unit ◽  
Vehicle Navigation ◽  
Real Time Kinematic ◽  
Autonomous Vehicle Navigation

Much navigation over the last several decades has been aided by the global navigation satellite system (GNSS). In addition, with the advent of the multi-GNSS era, more and more satellites are available for navigation purposes. However, the navigation is generally carried out by point positioning based on the pseudoranges. The real-time kinematic (RTK) and the advanced technology, namely, the network RTK (NRTK), were introduced for better positioning and navigation. Further improved navigation was also investigated by combining other sensors such as the inertial measurement unit (IMU). On the other hand, a deep learning technique has been recently evolving in many fields, including automatic navigation of the vehicles. This is because deep learning combines various sensors without complicated analytical modeling of each individual sensor. In this study, we structured the multilayer recurrent neural networks (RNN) to improve the accuracy and the stability of the GNSS absolute solutions for the autonomous vehicle navigation. Specifically, the long short-term memory (LSTM) is an especially useful algorithm for time series data such as navigation with moderate speed of platforms. From an experiment conducted in a testing area, the LSTM algorithm developed the positioning accuracy by about 40% compared to GNSS-only navigation without any external bias information. Once the bias is taken care of, the accuracy will significantly be improved up to 8 times better than the GNSS absolute positioning results. The bias terms of the solution need to be estimated within the model by optimizing the layers as well as the nodes each layer, which should be done in further research.

scholarly journals Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 151
Author(s):  
Harold R. Chamorro ◽  
Alvaro D. Orjuela-Cañón ◽  
David Ganger ◽  
Mattias Persson ◽  
Francisco Gonzalez-Longatt ◽  
...  
Keyword(s):  
Power Systems ◽  
Time Series Data ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Test System ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Measurement Unit ◽  
System Frequency

Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

scholarly journals A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6541
Author(s):  
So-Hyeon Jo ◽  
Joo Woo ◽  
Gi-Sig Byun ◽  
Baek-Soon Kwon ◽  
Jae-Hoon Jeong
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Occurrence Rate ◽  
Series Data ◽  
Measurement Unit ◽  
Judgment Accuracy ◽  
Personal Mobility ◽  
Long Short Term Memory ◽  
Mobility Device

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

Investigation of Forces in Deep Hole Bone Drilling

2018 ◽  
Author(s):  
JuEun Lee ◽  
Serena Chu ◽  
Craig L. Chavez
Keyword(s):  
Thrust Force ◽  
Early Stage ◽  
Chip Morphology ◽  
High Ratio ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drill Bit ◽  
Deep Hole ◽  
Bone Drilling ◽  
Drilling Depth

Deep hole drilling is required to install prosthetic devices in surgical implantation. Compared to the common bone drilling processes, deep hole bone drilling is performed with a larger hole depth (i.e., up to a depth of approximately 35 mm in cochlear implantation) using a high ratio of the length to diameter of the drill bit. For successful outcomes from this process, forces must be controlled adequately to avoid other complications such as drill-bit breakage or thermal necrosis. This study investigates the thrust force and torque generated in bone drilling process of up to 36 mm drilling depth. Drilling tests were performed on bovine cortical bone using 2.5 mm diameter twist drill bit with a spindle speed of 3000 rpm, and feed rates of 0.05, 0.075, and 0.1 mm/rev. Two distinct states in both the thrust force and torque data were observed for all conditions, which are called normal and abnormal states in this study. At an early stage of the drilling process, the force signals showed the traditional trend, reaching a constant value once the tip of the drill bit was fully engaged in bone cutting up to a certain depth. After that, both thrust force and torque kept increasing rapidly until the final drilling depth. This study also observed that the chip morphology varies with increasing drilling depth, showing fragmented chips at the normal state and powdery chips at the abnormal state. Chip clogging and increased frictional force between chips, tool, and hole wall with larger drilling depth may cause the abrupt increase in forces and variation in chip morphology.

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