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1

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.

2

Wykorzystanie bezzałogowych statków powietrznych w procesie zarządzania bezpieczeństwem pracy w budownictwie

Nowobilski Tomasz, Hoła Bożena

Przegląd Budowlany

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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.

3

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.

4

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

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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.

5

Generative modelling of vibration signals in machine maintenance

Puchalski Andrzej Adam, Komorska Iwona

Eksploatacja i Niezawodność

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2023

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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.

6

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

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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.

7

Fault diagnosis of high-speed rotating machines using MATLAB

Joshi Mahesh B., Pujari Kamlesh S.

Diagnostyka

|

2023

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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.

8

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

Khaire Pallavi, Phalle Vikas

Archive of Mechanical Engineering

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2023

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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%.

9

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

Augustyn Damian, Fidali Marek

Acta Mechanica et Automatica

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2023

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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.

10

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

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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.

11

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.

12

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.

13

Unsupervised detection of state changes during operation of machine elements

Hillenbrand Jonas, Fleischer Jürgen

Journal of Machine Engineering

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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.

14

Tooling systems with integrated sensors enabling data based process optimization

Bleicher Friedrich, Ramsauer Christoph, Leonhartsberger Martin, Lamprecht Matthias, Stadler Philipp, Strasser Dominik, Wiedermann Clemens

Journal of Machine Engineering

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2021

|

Vol. 21, No. 1

5--21

EN

Sensor integration into machining equipment has become an important factor for gaining deep process insights mainly driven by increasingly smaller and cheaper sensors and transmitters. Due to advances in microelectronics and communication technology, a broader field of applications in production processes and machine tools can be addressed using sensing devices and their implementation potentials. Ensuring a sensitive but robust data stream from close to the actual process allows not only reliable monitoring but also process and quality control based on sensor information. This paper provides an overview of the utilization of sensor data for the purpose of condition monitoring, model fitting and real-time control coping with stochastic effects. Examples of sensor integration in fields of injection molding, roll forming and heavy-duty milling comprise the state of the art of sensor implementation, data evaluation and possible feedback loops in the respective application scenarios.

15

Offline-online pattern recognition for enabling time series anomaly detection on older NC machine tools

Netzer Markus, Palenga Yannic, Goennheimer Philipp, Fleischer Juergen

Journal of Machine Engineering

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2021

|

Vol. 21, No. 1

98--108

EN

Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining.

16

New method for determining single cutting edge breakage of a multi-tooth milling tool based on acceleration measurements of an instrumented tool holder

Ramsauer Christoph, Bleicher Friedrich

Journal of Machine Engineering

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2021

|

Vol. 21, No. 1

67--77

EN

In machining applications predominantly for automated machining cells, tool life is often not used to its full extend and cutting tools are exchanged prematurely to avoid tool breakage and thus machine downtime or even damage at work piece or machine. Both effective process monitoring and adequate process control require reliable data from sensors and derived indicators that enable meaningful evaluation. Acceleration measurement by the instrumented tool holder provides signals with high quality from close to the cutting zone. Using the monitoring system, the gained data of the instrumented tool holder can be analyzed especially for the use case of unexpected tool wear, chipping of the cutting edge or breakouts at end mills. This paper describes the data analysis based on the rotational sensor and the corresponding effects on the measurement, an advanced assessment of the spectral distribution in the frequency domain and the experimental results of a test series.

17

A new non-intrusive condition monitoring system designed to improve reliability of RCDs

Machocki Krzysztof, Sehsah Ossama, Marhoon Zahrah, Dixon Tom, Amjad Shaarawi, Mohammad Jamal Ud-Din

Journal of Geotechnology and Energy

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2021

|

Vol. 38, no. 3

13--18

EN

Managed Pressure Drilling (MPD) is a technology that allows for precise wellbore pressure control, especially in formations with uncertain geomechanics. The Rotating Control Device (RCD) is a crucial part of the MPD equipment but is prone to failure. Therefore, a new condition monitoring system was developed to improve the reliability of RCDs and eliminate their catastrophic failures during MPD jobs. Non-intrusive sensors were selected during the design of this condition monitoring system. Sensors measure: vibrations, acoustic emissions, rotation, pipe movement, temperatures, and contamination level in the coolant fluid. The system can display the measurements in real-time to the operator, giving early warnings to prevent the RCD’s catastrophic failures during the job. Additionally, the data is recorded to allow further processing and analysis using ML and AI techniques.

18

Integrating advanced measurement and signal processing for reliability decision-making

Kozłowski Edward, Antosz Katarzyna, Mazurkiewicz Dariusz, Sęp Jarosław, Żabiński Tomasz

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 4

777--787

EN

An advanced milling machine multi-sensor measurement system as a condition monitoring tool was presented. It was assumed that the data collected from the 3-axis force and torque sensor can be used as a new approach and an alternative to the typical vibration signal based health monitoring and remaining useful life prediction (RUL), when integrated with machine learning techniques that are regarded as a powerful solution. Measurement system integration with the proposed signal processing method based on decision trees with different types and levels of wavelets for the cutter reliability decision-making process was presented together with proving their ability to trace the tool condition accurately. Prediction errors achieved with the use of different signal sources and data processing methods were presented and compared.

19

Diagnosis of air compressor condition using minimum redunduncy maximum relevance (MRMR) algorithim and distance metric based classification

Al-Bugharbee Hussein, Samaka Hatem, Zubaidi Salah L.

Diagnostyka

|

2021

|

Vol. 22, No. 4

25--32

EN

Finding a reliable machines condition monitoring technique has been attracted many researchers to avoid the sudden failure in machines and the unexpected consequences. This work proposes a fault diagnosis of air compressors using frequency-based features and distance metric-based classification. The analyzed experimental datasets contain one healthy condition and seven different fault conditions. Features are extracted from the frequency spectrum, then the best feature sets are selected using MRMR algorithm and eventually the classification is conducted using a distance metric classifier. The results demonstrated the automatic classification with more than 97% correct classification rate. The effect of selected feature set size, training sample size on the classification accuracy is also investigated. From the results, this method of analysis can be used for early detection of faults with very great accuracy.

20

An IoT based condition monitoring system of biogas electrical generator for performance evaluation

Chinh Hoang Duc, Anh Hoang, Hieu Nguyen Duy, Anh Vu The, Krishnanand Kaippilly Radhakrishnan

Annals of Computer Science and Information Systems

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2021

|

Vol. 27

7--11

EN

Biogas is a promising renewable energy source having great potential, especially in livestock farms. However, as biogas electric generators are usually deployed in rural areas, it would take more time and effort to repair if any fault occurs. Remote monitoring of the system condition is essential to diagnose or even predict the faults in advance and subsequently plan the maintenance schedule in time. This paper presents a monitoring system of biogas-based power generation system using Internet-of-Things (IoT) devices. Information of the generator operation is acquired by field devices and forwarded to a remote server. Data collection and management are facilitated by Lambda architecture and Apache Kafka software platform for their interoperability and strong support of big data management. The system shows that near real-time supervision of the object conditions can be obtained. Historical data analyses of a few operation scenarios are also provided to evaluate the generation system performance as well as to discuss its fault diagnosis.