An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Self Cite

In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

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“…On the other hand, CR communication is a promising technology to deal with spectrum allocation problems. CR is an intelligent device that scans the spectrum around its vicinity and searches vacant spectrum locations both in time and frequency which are not being utilized by licensed users also known as primary users (Pus) [5]. The cognitive radio network (CRN) connects cognitive radios that share the vacant spectrum among unlicensed Sensors 2022, 22, 451 2 of 14 users also known as secondary users (Sus), provided that they do not create interference with Pus [6].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

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The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

“…Machine learning techniques are also applied to motor fault detection, as is shown in [ 10 , 11 , 12 ]. The authors in [ 10 ] developed a method using twenty-one statistical calculations in time and frequency domain obtained from vibration signals with five thousand samples.…”

Section: Introductionmentioning

confidence: 99%

Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors

Contreras-Hernandez

Almanza-Ojeda

Ledesma

et al. 2022

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.