Detectivity: A combination of Hjorth’s parameters for condition monitoring of ball bearings

Cocconcelli, Marco; Strozzi, Matteo; Molano, Jacopo Cavalaglio Camargo; Rubini, Riccardo

doi:10.1016/j.ymssp.2021.108247

Cited by 26 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Hjorth parameters are found using the Hjorth function from the MATS toolkit [ 38 ]. Hjorth parameters are advanced statistical methods in observing time-domain signals through patterns in variance, making this a popular method in monitoring EEG data [ 39 ]. The entropy features describe the amount of uncertainty in the EEG signal pattern.…”

Section: Methodsmentioning

confidence: 99%

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Ketola

Barankovich

Schuckers

et al. 2022

Sensors

View full text Add to dashboard Cite

Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32–64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks.

show abstract

Section: Methodsmentioning

confidence: 99%

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Ketola

Barankovich

Schuckers

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…It gives an estimate of the bandwidth of the signal and indicates the resemblance of the signal with a pure sinusoid. A more detailed description of the three Hjorth's parameters can be found in Hjorth, 33 and Cocconcelli, et al 41…”

Section: Mobility(x) =mentioning

confidence: 99%

“…33 Earlier, these parameters were more common in biomedical fields, 34 however, with recognized potential of fault characterization, the Hjorth's parameters have been successfully applied in some of the recent research works. [35][36][37][38][39][40][41] Caesarendra and Tjahjowidodo 35 studied the changes in statistical features, including Hjorth's parameters, as the natural fault develops in slow speed slew bearings, and found that impulse factor, margin factor, approximate entropy and largest Lyapunov exponent possess the fault indicative potentials. Among Hjorth's three parameters, only activity was found sensitive to the occurrence of fault.…”

Section: Introductionmentioning

confidence: 99%

“…Liang and Lu 39 performed Variational Mode decomposition (VMD), and extracted the features including Rényi entropy, singular value and Hjorth's parameters, from the best mode. Recently, Cocconcelli 41 devised a new health indicator called 'Detectivity' by combining three Hjorth's parameters to enhance the fault detection potential of Hjorth's parameters. The derived parameter was interpreted as an electric combination of three components in series: with activity and complexity as two amplifiers and mobility as an attenuator.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the Classification Performance of Machine Learning Techniques by Using Hjorth's and Other Statistical Parameters for Precise Tracking of Naturally Evolving Faults in Ball Bearings

Mufazzal¹,

Muzakkir²,

Khanam³

2022

The International Journal of Acoustics and Vibration

View full text Add to dashboard Cite

The research on identification of artificially induced faults in bearing is available in abundance in the past literature, however, the diagnosis becomes more challenging when the fault evolves naturally inside the bearing, and especially when its stages need to be precisely tracked. The conventional statistical features used commonly in the past literature do not uniquely characterize the fault status, and yield satisfactory results only in limited cases, like those for artificial faults. In this work, a new combination of fault descriptors, including three Hjorth's parameters, three statistical features and an entropy measure, is proposed and its effectiveness has been analyzed on classification performances of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Two datasets comprising the signals of run-to-failure tests were taken from Intelligent Maintenance Systems (IMS) and the Paderborn university repository. The data were categorized into large number of classes to closely indicate the actual fault type and size. Compared to conventional statistical features, the new combination was able to enhance the classification accuracy of k-NN and SVM, respectively, from 91.3% to 99.9%, and from 94.8% to 99.7% in the case of IMS dataset, and from 94.1% to 98.5%, and from 94.7% to 98.4% in the case of Paderborn dataset. In addition to accuracy, the performance metrics, including precision, recall, and F1-score were also improved using the proposed combinatory features.

show abstract

“…Hjorth parameters have been successfully applied in many acoustic monitoring scenarios [ 24 , 25 ], which make them good candidates to capture the most important characteristics of the input signal. In addition, the autoencoder DL approach demands a complementary tool to assign a threshold or to monitor the time-series of errors produced by the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm

Ozelim

Borges

Cavalcante

et al. 2022

Sensors

View full text Add to dashboard Cite

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow.

show abstract

Detectivity: A combination of Hjorth’s parameters for condition monitoring of ball bearings

Cited by 26 publications

References 22 publications

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Enhancing the Classification Performance of Machine Learning Techniques by Using Hjorth's and Other Statistical Parameters for Precise Tracking of Naturally Evolving Faults in Ball Bearings

Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm

Contact Info

Product

Resources

About