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Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a signi...
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Este artículo se enmarca dentro del área de diagnóstico y detección de fallas en procesos industriales, enfocando su aplicación hacia los motores síncronos para identificar las fallas incipientes que afectan las principales ventajas de utilización de estos motores. En la actualidad, existen muchos métodos de detección y diagnóstico de fallas, pero...
It is well-known that, music stimuli have powerful emotion trigger effect.
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Motor overheating is a serious problem, which can be caused by overload, poor maintenance, age degradation, among other reasons. If necessary precaution measures are not taken, it can result in premature damage of the motor or accidents. Monitoring the equipment condition, in this case the temperature, is a good way to prevent failures and enlarge...
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... The LeNet architecture, one of the convolutional neural networks, was introduced by Le Cun [37] and continued to be improved until 1998 [42]. LeNet was first proposed for digit classification, and then it was accepted to achieve promising performance in many application fields [43]. ...
Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy.
... (3) The convolutional neural network (CNN), as a plunger pump-monitoring signal feature extractor, lacks the ability to capture the features of signal timing on a timescale, so it is necessary to conduct in-depth studies on this aspect [17,18]. ...
... Sci. 2024, 14, x FOR PEER REVIEW 3 of 17 (3) The convolutional neural network (CNN), as a plunger pump-monitoring signal feature extractor, lacks the ability to capture the features of signal timing on a time-scale, so it is necessary to conduct in-depth studies on this aspect [17,18]. ...
Mechanical condition monitoring data in real engineering are often severely unbalanced, which can lead to a decrease in the stability and accuracy of intelligent diagnosis methods. In this paper, a fault diagnosis method based on the SMOTE + Tomek Link and dual-channel feature fusion is proposed to improve the performance of the sample imbalance fault diagnosis method, taking the piston pump of a turnout rutting machine as the research object. Combining the data undersampling method and the oversampling method to redistribute the collected normal data and fault data makes the diagnostic model have better diagnostic performance in the case of insufficient fault samples. And, in order to fully utilize the global features and local features, a global–local feature complementary module (GLFC) is proposed. Firstly, the generated data similar to the original data are constructed using the SMOTE + Tomek Link method; secondly, the generated data are input into a GLFC module and BiGRU at the same time, the GLFC module extracts the spatial global features and local features of the original vibration data, and BiGRU extracts the temporal information features of the original vibration data, and fuses the extracted feature information, and inputs the fused features into the attention layer; finally, a GLFC module is proposed by the SMOTE + Tomek Link method to make full use of the global features and local features. The extracted feature information is fused, and the fused features are input to the attention layer; finally, the fault classification is completed by the softmax classifier. In this paper, the accuracy and robustness of the proposed model are demonstrated through experiments.
... Articles CWRU bearing [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89] PU bearing [38,90,59,91,92,73,93,81,94,95] IMS bearing [32,44,96,97,60,63,98 [101,123,124,125,102] UO Bearing Dataset [101,72] UORED-VAFCLS Dataset [126] Gearbox Fault Diagnosis Data [73] MaFaulDa [82] MFS and In-Lab Dataset [127,45,48,50,52,53,54,55,56,128,129,130,46,131,132,133,111,97,57,134,135,60,136,137,66,71,77,138,79] NREL wind Turbine [80] SCADA [139,140,141,141,142] Other Datasets HOUDE Dataset [63], Ball Screw Dataset [63], SDU dataset [69], UNSW turbine blade dataset [82], Qianpeng Company Gearbox Dataset [94], NCEPU gear dataset [87], QPZ-II [143], QPZZ-II [144], Locomotive Bearing Dataset [65] ...
... Vibration data, as previously mentioned, is the prevalent data type within this strategy. [98] employed a 1-D CNN (3 convolution and 2 multi-layer perceptron layers) for bearing fault classification of the IMS dataset. The raw vibration data undergoes several essential preprocessing steps. ...
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many articles. Despite the availability of numerous solutions and reviews on MFD, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for condition-based analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for MFD and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe this article will help researchers and contribute to the further development of the field.
... Several types of ANN were adopted in bearings troubleshooting and condition-based maintenance (CBM), such as convolution neural networks (CNN) and recurrent neural networks (RNN). Eren, L. [17] presented a one-dimensional CNN model to monitor bearings health using a single-learning body model and achieved 97% fault detection accuracy. Hoang and Kang [18] used a novel CNN model that transforms 1D signals into 2D ones and approached 100% accuracy in defect detection using the Case Wester Reverse University (CWRU) public bearing data set. ...
... Many of these time domain parameters were obtained from the data and implemented in the proposed model. Of all time domain indicators in literature [78][79][80][81], seventeen (17) were used in training and testing the current work's model. Their formulas and abbreviations are displayed in Table 1. ...
This research introduces a groundbreaking method for bearing defect detection. It leverages ensemble machine learning (ML) models and conducts comprehensive feature importance analysis. The key innovation is the training and benchmarking of three tree ensemble models—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—on an extensive experimental dataset (QU-DMBF) collected from bearing tests with seeded defects of varying sizes on the inner and outer raceways under different operating conditions.
The dataset was meticulously prepared with categorical variable encoding and Min–Max data normalization to ensure consistent class distribution and model accuracy. Implementing the ML models involved a grid search method for hyperparameter tuning, focusing on reporting the models’ accuracy. The study also explores applying ensemble methods and using supervised and unsupervised learning algorithms for bearing fault detection. It underscores the value of feature importance analysis in understanding the contributions of specific inputs to the model’s performance. The research compares the ML models to traditional methods and discusses their potential for advanced fault diagnosis in bearing systems.
The XGBoost model, trained on data from actual bearing tests, outperformed the others, achieving 92% accuracy in detecting bearing health and fault location. However, a deeper analysis of feature importance reveals that the models weigh certain experimental conditions differently—such as sensor location and motor speed. This research’s primary novelties and contributions are comparative evaluation, experimental validation, accuracy benchmarking, and interpretable feature importance analysis. This comprehensive methodology advances the bearing health monitoring field and has significant practical implications for condition-based maintenance, potentially leading to substantial cost savings and improved operational efficiency.
... The output variable (Y) denotes the binary class assignment (normal or tumor). Employing the sigmoid activation function σ (z) = 1 1+exp(−z) , the model transforms the linear combination of input features into probabilities, ensuring outputs fall within the 0 to 1 range, signifying the likelihood of belonging to the positive class (tumor) [18][19][20]. The linear combination, expressed as the logit function [20] p = β 0 + β 1 X 1 + β 2 X 2 + . . . ...
Early cancer detection is crucial, especially for intestinal cancer with subtle early symptoms. While camera-based Wireless Capsule Endoscopy (WCE) systems are efficient, patient-friendly, and safe investigating gastrointestinal (GI) track thoroughly, some limitations persist in visualizing only the inner part of the GI regions. Our study introduces a radio channel analysis -based approach to detect intestinal/abdominal tumors which are not visible for the WCE camera, i.e., the tumors which have started to grow on the outer parts of the intestinal track. Focused on S-parameter patterns in realistic human voxel models, our simulation-based method discerns dielectric property variations in normal and tumorous tissues, replicating intricate tissue characteristics. Preliminary simulation results in different intestine locations demonstrate our technique’s efficacy in differentiating normal and tumor cases based on S-parameter patterns. With a 98% accuracy rate, simple logistic regression classification model excels in distinguishing normal from tumor tissues, significantly enhancing diagnostic precision in GI health monitoring showcasing its potential to revolutionize early cancer detection and advance diagnostic accuracy within simulated human anatomy. This represents a substantial stride toward improving healthcare outcomes through cutting-edge technology.
... The fault diagnosis of machinery systems has been made more effective using 1D-CNN. It has been shown to offer better accuracy than other methods and has also helped improve decision-making [58,59]. The process presented in this study leverages the strength of 1D-CNN to classify multiple faults separated from the complex mixture of signals acquired from machinery data. ...
Machinery health monitoring techniques provide valuable insights into the performance and condition of machines. Acoustic sensor-based monitoring has emerged as a significant area of interest for the industry due to its ability to accurately capture fault signatures, thereby improving the detection accuracies of anomalies or deviations from regular operations. However, the collected sensor signals typically contain a complex mixture of sounds that relate to multiple fault conditions, environmental noise, and other unwanted sounds from the surroundings. Identifying the specific root causes of failures is a challenge in modeling without knowledge of the unique characteristics of failure conditions. This can ultimately degrade the model’s performance or yield inaccurate failure estimations in condition monitoring, which is a consistent concern in the industry. Therefore, this study proposes a novel framework that enhances the accuracy of machinery fault diagnosis using audio source separation of complex mixture of sound signals. The proposed approach employs a Deep Extractor for Music Source Separation (DEMUCS), a state-of-the-art music source separation approach consisting of an encoder-decoder architecture that uses bi-directional long-short-term memory (LSTM) for industrial machine sound separation and enhancement. The proposed methodology comprises two steps. In the first step, the fault sound isolation and recovering individual fault sounds from a complex mixture of sound signals are enabled using DEMUCS. In the second step, the isolated fault sounds are fed through a 1D-convolutional neural network (1D-CNN) classifier for adequate classification. A machine fault simulator by Spectra Quest equipped with a condenser mic was employed to evaluate the proposed DEMUCS-CNN methodology for identifying multiple faults. The effectiveness of the DEMUCS-CNN method was also compared to the traditional approach of blind source separation (BSS). The outcomes of the comparison indicated that the suggested approach of fault isolation by DEMUCS led to enhanced fault classification accuracy, making it a more effective approach compared to conventional BSS.
... Another rapidly developing modern research area is artificial neural networks (ANN) and fuzzy logic algorithms, which among other things are also applied in sensor networks [19][20][21][22]. Artificial neural networks are also used as a control tool [22] and in signal processing devices [23,24]. In [25], an ANN was used to effectively analyze the effect of temperature and acidity level (pH) on the sensor sensitivity, providing a stable response under different physical conditions. ...
In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C.
... This integration allows for the collection and analysis of large amounts of data from machines, which can be used to optimize maintenance activities and improve the reliability and availability of machinery. Besides that, vibration signals and their analysis have become an integral part of every industrial plant worldwide, and ML techniques, either classical [1][2][3] or deep learning [4][5][6] models have been employed extensively for fault detection using these signals. The vibration signals from rotating equipment can be gathered easily during operation, reflecting their operating conditions in real-time, thus, current trends in machinery fault diagnostics mainly rely on vibration signal analysis [7]. ...
In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.
... Since 2015, fault diagnosis algorithms based on deep learning (DL) have become a hot spot [13]. Among these algorithms, the 1D CNN is particularly suitable for the overall optimization of each link of fault diagnosis and the improvement of the intelligent level of diagnosis due to its powerful data abstraction, end-to-end structure, and adaptive learning feature ability based on 1D vibrational signals [13]- [17]. To make the 1D CNN model better fit the characteristics of intelligent fault diagnosis based on vibration signals, the improvement of the model and algorithm is mainly carried out in two directions. ...
Previous 1D convolutional neural networks (1D CNN) models for vibration fault diagnosis have high computational complexity and poor interpretability, which cannot meet the higher requirements of model storage, computational efficiency and reliability for airborne and portable devices. Considering these challenges, an Explainable and Lightweight 1D CNN (ELCNN) model based on Square Global Average Pooling (S-GAP) and improved for vibration signals is proposed. The feature extraction and classification layers of 1D CNN are optimized to minimize the model parameters and computational complexity and improve interpretability while ensuring diagnostic accuracy. The model compresses the number of convolutional layers, removes unnecessary bias, activation function and pooling layer, and replaces the fully connected layer (FCL) with S-GAP. Improved 1D CNN models of different methods are evaluated and analyzed on public datasets of rolling bearings. Results show that the ELCNN improved for vibration signals is more lightweight, anti-noise and explainable than other models, and the diagnostic accuracy is further improved.
... In recent years, researchers have introduced one-dimensional convolutional neural networks (1D-CNNs) to cater more effectively to onedimensional time-series signals. [52][53][54] Due to their ability to directly extract features from 1D signals and their advantages in terms of computational efficiency and accuracy, 1D-CNNs have garnered significant attention from the research community. Abdeljaber et al. 55 proposed a compact 1D-CNN method for online monitoring and assessing the severity of bearing damage, which exhibits low hardware requirements, high detection accuracy, and strong noise resistance. ...
Pipeline networks are crucial components of modern infrastructure, and ensuring their reliable operation is essential for sustainable development. The percussion-based methods are considered promising for detecting pipeline faults due to their avoidance of constant-contact sensors and ease of implementation. However, the majority of existing percussion-based methods suffer from limitations such as the requirement for manual feature extraction, as well as subpar noise resilience and adaptability. This paper introduces a one-dimensional convolutional bidirectional long short-term memory network with wide first-layer kernels for the classification of percussion-induced acoustic signals, thus achieving automatic identification of pipeline leakage and water deposit conditions. This approach directly extracts features from audio signals using wide first-layer convolutional kernels, eliminating the need for manual feature extraction. Additionally, it employs bidirectional long short-term memory to effectively capture long-term signal dependencies from both past and future contexts. To validate the effectiveness of the method, two case studies were conducted on three groups of pipes. The results show that the proposed method demonstrates superior noise resistance and adaptability compared to other methods, and it also exhibits strong applicability to other percussion signal datasets. Additionally, the impact of different first convolutional kernel sizes on the noise resistance and adaptive performance of the model was investigated, which provides robust guidance for the effective processing of percussion-induced acoustic signals.
