A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals

Abstract

Machine fault diagnosis (MFD) has gained an important enthusiasm since the unfolding of the pattern recognition techniques in the last three decades. It refers to all of the studies that aim to automatically detect the faults on the machines using various kinds of signals that they can generate. The present work proposes a MFD system for the drilling machines that is based on the sounds they produce. The first key contribution of this paper is to present a system specifically designed for the drills, by attempting not only to detect the faulty drills but also to detect whether the sounds were generated during the active or the idling stage of the whole machinery system, in order to provide a complete remote control. The second key contribution of the work is to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. The created images, the so-called power spectrum density (PSD)-images, are then given to a deep convolutional autoencoder (DCAE) for a high-level feature extraction process. The final step of the scheme consists of adopting the proposed PSD-images + DCAE features as the final representation of the original sounds and utilize them as the inputs of a nonlinear classifier whose outputs will represent the final diagnosis decision. The results of the experiments demonstrate the high discrimination potential afforded by the proposed PSD-images + DCAE features. They were also tested on a noisy dataset and the results show their robustness against noises.

One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

“…This only proves that when it comes to artificial intelligence, the mining field, especially in exploitation, is lagging compared to other fields such as the petroleum exploration and production (P&E) field, as well as the medical field. Most studies that utilize machine learning to monitor the condition of drill bits during drilling have been conducted in the manufacturing and P&E industry [10][11][12][13][14]. Although the drilling techniques in these industries are different from those of the mining industry, this study believes that some concepts should be adopted and mirrored to enhance the efficiency of drilling operations in the mining industry.…”

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning

“…As a countermeasure, in the medical field, a population with a higher pre-test probability in other items can be extracted and applied to the judgment machine. In this arteriovenous fistula sound classification approach, a large classifier that combines the classical statistical method based on physical sound features [ 26 ] and a learning model that detects abnormal sounds by unsupervised learning with this learning model is considered to be an effective classification method [ 27 , 28 , 29 ].…”

Peran Al-Quran Dalam Meningkatkan Pemahaman Pembelajaran Qur’an Hadits

“…Pada pembelajaran Qur'an hadits ini siswa wajibkan oleh guru untuk membawa A-Qur'an, karena Al-Qur'an merupakan (Sensi et al, 2019) objek kajian utama dalam pembelajaran Qur'an hadits. Selain dengan Al-Qur'an, guru yang mengajar di (Mu & Hatch, 2021) madrasah juga bisa menggunakan media pembelajaran yang bervariasi (Zimmerman et al, 2018) yang bisa membuat siswa itu tertarik dan semangat dalam belajar (Vununu et al, 2018), misalnya seperti pembelajaran itu berupa video (Hussain et al, 2021) yang dibuat seperti animasi dan ditampilkan dilayar didepan kelas, sehingga siswa tau bentuk-bentuk penjelasan yang disampaikan oleh guru. Bisa juga penyampaian materi itu berupa power point (Dunham et al, 2018) yang juga ditampilkan didepan kelas, dan setelahnya guru itu menjelaskan point-point (Brown & Wood, 2018) saja dan kemudian siswa diminta untuk mencatat point yang dijelaskan (Chung et al, 2019)tersebut.…”