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1

Utilizing convolutional autoencoders for anomaly detection and drill bit damage assessment in rotary percussion drilling

Senjoba Lesego, Ikeda Hajime, Toriya Hisatoshi, Adachi Tsuyoshi, Kawamura Youhei

Journal of Sustainable Mining

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2025

|

Vol. 24, iss. 2

282--298

EN

Monitoring drill bit wear during drilling improves operational efficiency. Wear in drill bits is influenced by factors related to drilling parameters and rock properties and can be categorized into types based on the cause of failure. Several studies have used machine learning to detect drill bit wear, mainly using classification models. However, these models require an extensive amount of data that includes different possible drill bit failures. This presents a challenge as acquiring data is difficult due to the scarcity of anomalies. This study introduces a novel approach utilizing convolutional autoencoders (CAE) for both anomaly detection and drill bit damage assessment within rotary percussion drilling. The proposed CAE model was trained on the vibrations of normal data, learning to accurately reconstruct input patterns. A dual threshold was set based on the reconstruction errors of the validation data, serving as a criterion to distinguish between normal and anomalous data. An innovative method for assessing drill bit degradation was developed, utilizing reconstruction errors and thresholds. These findings highlight the significance of adopting advanced machine learning techniques to optimize drilling performance and enhance operational efficiency within the mining field.

2

Zdalne monitorowanie stanu pojazdu w czasie rzeczywistym

Murawski Krzysztof, Górski Tomasz

Przegląd Teleinformatyczny

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2024

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T. 12, Nr 1-4

41--68

PL

W artykule przedstawiono sposób monitorowania stanu pojazdu realizowany w czasie rzeczywistym. Wskazano rozwiązanie techniczne, które to monitorowanie umożliwia oraz problemy, które należało rozwiązać. Wytworzone urządzenie umożliwia monitorowanie stanu dowolnego obiektu wyposażonego w magistralę OBD-II.

EN

The article presents the method of monitoring the condition of the vehicle carried out in real time. The technical solution that makes this monitoring possible and the problems that needed to be solved were indicated. The device allows you to monitor the status of any object equipped with an OBD-II bus.

3

Hybrid predictive maintenance model – study and implementation example

Wiercioch Jakub

Production Engineering Archives

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2024

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Vol. 30, Iss. 3

285--295

EN

In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re search conducted, it was determined which machine operating parameters are characterised by varia bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime.

4

Simulating Rolling Element Bearing Defects in Induction Machines

Floh Florian, Weiss Helmut, Makoschitz Markus

Power Electronics and Drives

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2024

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Vol. 9 (44)

540--561

EN

The significant occurrence of bearing faults in electrical machines necessitates continuous online monitoring of the machine’s operating data with the main objective of ensuring both high reliability and efficiency and therefore minimising the chance of unwanted breakdowns. This work focuses on the simulation of (defective) bearings, utilising a dedicated model with five degrees of freedom (DOF) (translational motion) in conjunction with an induction motor model. The primary objective is to gain a comprehensive understanding of how faulty bearings influence both the entire bearing itself and the machine, mainly concerning vibration signals and additional frictional torque. Additionally, various shapes of spalls on the raceway(s) are described, analysed and compared. This work is an extended version of the conference paper ‘Simulating Rolling Element Bearing Defects in Induction Machines’, presenting additional information on how to simulate spalls (with different shapes and sizes) on the inner ring of the bearing. Furthermore, the so-obtained vibration signal is examined and a method is proposed aiming to verify the simulation results and to predict the location of the spall (raceway of the inner or outer ring).

5

System monitoringu elementów rozdzielnicy SN pod napięciem

Kucharek Mariusz, Książek Leszek, Radziak Kamil, Michalski Paweł, Lisowiec Aleksander

Elektro Info

|

2024

|

nr 10

32--34

PL

W artykule opisano system eTemp służący do monitoringu elementów pod napięciem, których temperatura może wzrosnąć w wyniku zwiększenia rezystancji połączeń skręcanych, przewężeń, połączeń wykonawczych, głowic kablowych oraz w wyniku słabej wentylacji lub długotrwałego przekroczenia prądów znamionowych rozdzielnicy. Sensory temperatury, instalowane na elementach będących pod napięciem, pobierają energię zasilającą z pola elektromagnetycznego wokół przewodów z prądem. System eTemp składa się z sieci takich sensorów do pomiaru temperatury elementów znajdujących się w strefie napięcia niebezpiecznego oraz sensorów temperatury i wilgotności powietrza zasilanych bateryjnie.

EN

In the paper a system, called eTemp, for monitoring the temperature of switchboard elements, like busbars, cable connectors, has been described. Temperature sensors installed on the busbars are powered from the electromagnetic field around the current conductors. The eTemp system consists of temperature sensors located in hazardous area and temperature and humidity sensors that are battery powered.

6

Research on Online Condition monitoring for Complex System based on Modified Broad Learning Systems

Wang Chong, Liu Jie

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 2

art. no. 183600

EN

Complex systems contain numerous interacting components, thus deep learning methods with powerful performance and complex structure are often used to achieve condition monitoring. However, the deep learning methods are always too time-consuming and hardware-demanding to be loaded into complex systems for online training and updates. To achieve accurate and timely monitoring of complex system state, based on broad learning system (BLS), an online condition monitoring method is proposed in this paper. GeneralBLSs are based on a randomly generated hidden-layer, usually perform poorly in high-dimensional data classification tasks. In this work, based on correlation and causality, two modified BLSs are proposed and mixed to establish the online monitoring system. Specifically, logistic regression (LR) and structural causal model (SCM) are considered to form rough predictions of the system state, thus to replace the randomly generated ones with no practical significance. The effectiveness of the proposed online monitoring method is verified by both simulation data and real data.

7

Fault Diagnosis of Suspension System Based on Spectrogram Image and Vision Transformer

Arun Balaji, Venkatesh Naveen S, Sugumaran V

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 174860

EN

The suspension system in an automobile is essential for comfort and control. Implementing a monitoring system is crucial to ensure proper function, prevent accidents, maintain performance, and reduce both downtime and costs. Traditionally, diagnosing faults in suspension systems has relied on specialized setups and vibration analysis. The conventional approach typically involves either wavelet analysis or a machine learning approach. While these methods are effective, they often demand specialized expertise and time consumable. Alternatively, using deep learning for suspension system fault diagnosis enables faster and more precise real-time fault detection. This study explores the use of vision transformers as an innovative approach to fault diagnosis in suspension systems, utilizing spectrogram images. The process involves extracting spectrogram images from vibration signals, which serve as inputs for the vision transformer model. The test results demonstrate that the proposed fault diagnosis system achieves an impressive accuracy rate of 98.12% in identifying faults.

8

Classification of leakage current odd harmonic component of aged insulator surface condition under different relative humidity using crest factor

Benisheikh Umar, Abdul-Malek Zulkurnain, Sahito Ayaz

Archives of Electrical Engineering

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2024

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Vol. 73, nr 4

1069--1085

EN

The impact of pollution on insulator surface performance has been extensively studied to continuously improve the insulator performance using suitable condition monitoring, including using the leakage current harmonic component analysis. However, the analysis of harmonic components, particularly in relation to the odd harmonics of the leakage current in aged insulators under the influence of contamination and humidity, is still not fully understood and remains deficient. In this paper, the leakage current of aged and polluted insulators under different environmental conditions are investigated. The study was conducted experimentally using twelve samples of insulators with varying degrees of aging. The crest factor was employed as an indicator of the leakage current index. The findings showed that the odd harmonic crest factor demonstrates high sensitivity in classifying the degree of aging in contaminated insulators.

9

Wykorzystanie bezzałogowych statków powietrznych w procesie zarządzania bezpieczeństwem pracy w budownictwie

Nowobilski Tomasz, Hoła Bożena

Przegląd Budowlany

|

2023

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R. 94, nr 9-10

126--130

PL

Bardzo istotnym elementem wpływającym na poziom bezpieczeństwa pracy w budownictwie jest stosowanie właściwych narzędzi wspomagających zarządzanie bezpieczeństwem pracy podczas prowadzenia prac budowlanych. W ostatnich latach coraz częściej wykorzystuje się w tym celu nowe technologie, do których należy zaliczyć bezzałogowe statki powietrzne. Celem artykułu jest analiza możliwości wykorzystania dronów w procesie zarządzania bezpieczeństwem pracy w budownictwie, z uwzględnieniem korzyści i ograniczeń płynących z zastosowania tej technologii w praktyce. Dodatkowo autorzy artykułu zwrócili również uwagę na nowe, niewystępujące dotychczas, źródła zagrożeń dla bezpieczeństwa pracy jakie generuje wykorzystanie dronów w procesie budowlanym.

EN

A very important element affecting the level of occupational safety in construction is the use of appropriate tools to assist in the management of safety during construction work. In recent years, new technologies, which include unmanned aerial vehicles, have been increasingly used for this purpose. This article aims to analyse the possibility of using drones in the construction safety management process, taking into account the benefits and limitations of using this technology in practice. In addition, the authors of the article also highlighted new sources of occupational safety risks generated by the use of drones in the construction process, which have not occurred before.

10

An experimental study of the effects of cylinder lubricating oils on the vibration characteristics of a two-stroke low-speed marine diesel engine

Wu Gang, Jiang Guodong, Chen Changsheng, Jiang Guohe, Pu Xigang, Chen Biwen

Polish Maritime Research

|

2023

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nr 4

92--101

EN

Two-stroke, low-speed diesel engines are widely used in large ships due to their good performance and fuel economy. However, there have been few studies of the effects of lubricating oils on the vibration of two-stroke, low-speed diesel engines. In this work, the effects of three different lubricating oils on the vibration characteristics of a low-speed engine are investigated, using the frequency domain, time-frequency domain, fast Fourier transform (FFT) and short-time Fourier transform (STFT) methods. The results show that non-invasive condition monitoring of the wear to a cylinder liner in a low-speed marine engine can be successfully achieved based on vibration signals. Both the FFT and STFT methods are capable of capturing information about combustion in the cylinder online in real time, and the STFT method also provides the ability to visualise the results with more comprehensive information. From the online condition monitoring of vibration signals, cylinder lubricants with medium viscosity and medium alkali content are found to have the best wear protection properties. This result is consistent with those of an elemental analysis of cylinder lubrication properties and an analysis of the data measured from a piston lifted from the cylinder after 300 h of engine operation.

11

Multi-feature spatial distribution alignment enhanced domain adaptive method for tool condition monitoring

Hei Zhendong, Sun Bintao, Wang Gaonghai, Lou Yongjian, Yuqing Zhou

Eksploatacja i Niezawodność

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2023

|

Vol. 25, no. 4

art. no. 171750

EN

Transfer learning (TL) has been successfully implemented in tool condition monitoring (TCM) to address the lack of labeled data in real industrial scenarios. In current TL models, the domain offset in the joint distribution of input feature and output label still exists after the feature distribution of the two domains is aligned, resulting in performance degradation. A multiple feature spatial distribution alignment (MSDA) method is proposed, Including Correlation alignment for deep domain adaptation (DeepCORAL) and Joint maximum mean difference (JMMD). Deep CORAL is employed to learn nonlinear transformations, align source and target domains at the feature level through the second-order statistical correlations. JMMD is applied to improve domain alignmentby aligning the joint distribution of input features and output labels. ResNet18 combining with bidirectional short-term memory network and attention mechanism is developed to extract the invariant features. TCM experiments with four transfer tasks were conducted and demonstrated the effectiveness of the proposed method.

12

Generative modelling of vibration signals in machine maintenance

Puchalski Andrzej Adam, Komorska Iwona

Eksploatacja i Niezawodność

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2023

|

Vol. 25, no. 4

art. no. 173488

EN

The exponential development of technologies for the acquisition, collection, and processing of data from real-world objects is creating new perspectives in the field of machine maintenance. The Industrial Internet of Things is the source of a huge collection of measurement data. The performance of classification or regression algorithms needs to take into account the random nature of the process being modelled and any incomplete observability, especially in terms of failure states. The article highlights the practical possibilities of using generative artificial intelligence and deep machine learning systems to create synthetic measurement observations in monitoring the vibrations of rotating machinery to improve unbalanced databases. Variational Autoencoder VAE generative models with latent variables in the form of high-level input features of time-frequency spectra were studied. The mapping and generation algorithm was optimised and its effectiveness was tested in the practical solution of the task of diagnosing the three operating states of a demonstration gearbox.

13

Camera-based PHM method in rotating machinery equipment micro-action scenarios

Junfeng An, Liu Jiqiang, Zhen Hao, Mengmeng Lu

Eksploatacja i Niezawodność

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2023

|

Vol. 25, no. 1

art. no. 10

EN

The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.

14

Fault diagnosis of high-speed rotating machines using MATLAB

Joshi Mahesh B., Pujari Kamlesh S.

Diagnostyka

|

2023

|

Vol. 24, No. 2

art. no. 2023208

EN

Industrial high-speed rotating machines entail constant and consistent monitoring to prevent downtime, affecting quantity and quality. Complex machines need advanced intelligent fault diagnosis showing minimal errors. This work offers a MATLAB-based fault diagnosis for sugar industry machines. The vibration behavior of physical industrial machines is obtained, and the signals are provided to a MATLAB program to identify the fault. The information helps to suggest remedies to include in the maintenance schedule. The ease and comprehensible nature of the method reduce time and enhance the reliability of condition monitoring for industrial machines.

15

A smart fault identification system for ball bearing using simulation-driven vibration analysis

Khaire Pallavi, Phalle Vikas

Archive of Mechanical Engineering

|

2023

|

LXX, nr 2

247--270

EN

Bearings are one of the pivotal parts of rotating machines. The health of a bearing is responsible for the hassle-free operation of a machine. As vibration signatures give intimations of machine failure at an earlier stage, mostly vibration-based condition monitoring is used to monitor bearing’s health for avoiding the risk of failure. In this work, a simulation-based approach is adopted to identify surface defects at ball bearing raceways. The vibration data in time and frequency domain is captured by FFT analyzer from an experimental setup. The time frequency domain conversion of a raw time domain data was carried out by wavelet packet transform, as it takes into account the transients and spectral frequencies. The rotor bearing model is simulated in Ansys. Finally, most influencing statistical features were extracted by employing Principal Component Analysis (PCA), and fed to Multiclass Support Vector Machine (MSVM). To train the algorithm, the simulated data is used whereas the data acquired from FFT analyzer is used for testing. It can be concluded that the defects are characterized by Ball Pass Frequency (BPF) at inner race and outer raceway as indicated in the literature. The developed model is capable to monitor bearing’s health which gives an average accuracy of 99%.

16

Method of machining centre sliding system fault detection using torque signals and autoencoder

Augustyn Damian, Fidali Marek

Acta Mechanica et Automatica

|

2023

|

Vol. 17, no. 3

445--451

EN

The sliding system of machining centres often causes maintenance and process problems. Improper operation of the sliding system can result from wear of mechanical parts and drives faults. To detect the faulty operation of the sliding system, measurements of the torque of its servomotors can be used. Servomotor controllers can measure motor current, which can be used to calculate motor torque. For research purposes, the authors used a set of torque signals from the machining centre servomotors that were acquired over a long period. The signals were collected during a diagnostic test programmed in the machining centre controller and performed once per day. In this article, a method for detecting anomalies in torque signals was presented for the condition assessment of the machining centre sliding systems. During the research, an autoencoder was used to detect the anomaly, and the condition was assessed based on the value of the reconstruction error. The results indicate that the anomaly detection method using an autoencoder is an effective solution for detecting damage to the sliding system and can be easily used in a condition monitoring system.

17

Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform

Pietrzak Przemysław, Wolkiewicz Marcin

Power Electronics and Drives

|

2022

|

Vol. 7 (42)

112--133

EN

In modern drive systems, the high-efficient permanent magnet synchronous motors (PMSMs) have become one of the most substantial components. Nevertheless, such machines are exposed to various types of faults. Hence, on-line condition monitoring and fault diagnosis of PMSMs have become necessary. One of the most common PMSM faults is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that allow fault detection at its early stage. The article presents the results of experimental studies obtained from fast Fourier transform (FFT) and short-time Fourier transform (STFT) analyses of the stator phase current, stator phase current envelope and stator phase current space vector module. The superiority of the proposed method over the classical approach based on the stator current analysis using FFT is highlighted. The proposed solution is experimentally verified under various motor operating conditions. The application of STFT analysis discussed so far in the literature has been limited to the fault diagnosis of induction motors and the narrow range of the analysed motor operating conditions. Moreover, there are no works in the field of motor diagnostics dealing with STFT analysis for stator windings based on the stator current envelope and the stator current space vector module.

18

Primary testing of an instrumented tool holder for brush deburring of milled workpieces

Ramsauer Christoph, Oswald Ralf, Schörghofer Paul, Leder Norbert, Schmitz Tony, Bleicher Friedrich

Journal of Machine Engineering

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2022

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Vol. 22, No. 2

99--107

EN

Brush deburring requires consistent contact pressure between brush and workpiece. Automating adjustments to control contact pressure has proven difficult, as the sensors available in machine tools are usually not suitable to observe the small amplitude signals caused by this low force process. Additionally, both the power consumption and the vibration signal caused by the process strongly depend on the workpiece surface features. This paper describes a test setup using an instrumented tool holder and presents the corresponding measurement results, aiming to quantify the axial feed of the brush. It also discusses the interpretation of different signal components and provides an outlook on the utilization of the data for tool wear estimation.

19

Stator winding fault detection of permanent magnet synchronous motors based on the bispectrum analysis

Pietrzak Przemysław, Wolkiewicz Marcin

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 2

art. no. e140556

EN

The popularity of high-efficiency permanent magnet synchronous motors in drive systems has continued to grow in recent years. Therefore, also the detection of their faults is becoming a very important issue. The most common fault of this type of motor is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that facilitate damage detection in its early stages. This paper presents the effectiveness of spectral and bispectrum analysis application for the detection of stator winding faults in permanent magnet synchronous motors. The analyzed diagnostic signals are stator phase current, stator phase current envelope, and stator phase current space vector module. The proposed solution is experimentally verified during various motor operating conditions. The object of the experimental verification was a 2.5 kW permanent magnet synchronous motor, the construction of which was specially prepared to facilitate inter-turn short circuits modelling. The application of bispectrum analysis discussed so far in the literature has been limited to vibration signals and detecting mechanical damages. There are no papers in the field of motor diagnostic dealing with the bispectrum analysis for stator winding fault detection, especially based on stator phase current signal.

20

Unsupervised detection of state changes during operation of machine elements

Hillenbrand Jonas, Fleischer Jürgen

Journal of Machine Engineering

|

2021

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Vol. 21, No. 2

35--46

EN

Interpretation of sensor data from machine elements is challenging, if no prior knowledge of the system is available. Evaluation methods must adapt surrounding conditions and operation modes. As supervised learning models can be time-consuming to set up, unsupervised learning poses as alternative solution. This paper introduces a new clustering scheme that incorporates iterative cluster retrieval in order to track the clustering results over time. The approach is used to identify changing machine element states such as operating conditions and undesired changes, like incipient damage or wear. We show that knowledge about the evolving clusters can be used to identify operation and failure events. The approach is validated for machine elements with slide and roll contacts, such as ball screws and bearings. The data used has been captured using vibration and acoustic emission sensors. The results show a general applicability to the unsupervised monitoring of machine elements using the proposed approach.