Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures

Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e. normal bearing and three different types of defected bearings on outer race, inner race and roller separately. Particle Swarm Optimization (PSO) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modelling based Continuous Wavelet Transform (IMCWT) model. Then, a three-dimensional feature space of the statistical parameters and a Nearest Neighbor (NN) classifier are respectively applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment

An Intelligent System for Bearing Condition Monitoring

Rolling-element bearings are widely used in various mechanical and electrical applications. Accordingly, a reliable bearing health condition monitoring system is very useful in industries to detect incipient defects in bearings, so as to prevent machinery performance degradation and malfunction. Although several techniques have been reported in the literature for bearing fault detection and diagnosis, it is still challenging to implement a bearing condition monitoring system for real-world industrial applications because of the complexity of bearing structures and noisy operating conditions. The objective of this thesis is to develop a novel intelligent system for more reliable bearing fault diagnostics. This system involves two sequential processes: feature extraction and decision-making. The proposed strategy is to develop advanced and robust techniques at each processing stage so as to improve the reliability of bearing condition monitoring. First, a novel wavelet spectrum analysis technique is proposed for the representative feature extraction. This technique applies the wavelet transform to demodulate the resonance signatures that are related to bearing health conditions. A weighted Shannon function is proposed to synthesize the wavelet coefficient functions to enhance feature characteristics. The viability of this technique is verified by experimental tests corresponding to various bearing health conditions. Secondly, an enhanced diagnostic scheme is developed for automatic decision-making. This scheme consists of modules of classification and prediction: a novel neuro-fuzzy classifier is developed to effectively integrate the strengths of the selected fault detection techniques (i.e., the resulting representative features) for a more accurate assessment of bearing health conditions; a novel multi-step predictor is proposed to forecast the future states of bearing conditions, which will be used to further enhance the diagnostic reliability. The investigation results have demonstrated that the developed intelligent diagnostic system outperforms other related bearing fault diagnostic schemes

Fine-to-coarse multiscale permutation entropy for rolling bearing fault diagnosis

Multiscale Permutation Entropy (MPE) has been applied as a non-linear measure for estimating the complexity of time series. Nevertheless, the coarse-grained procedure in MPE only takes low-frequency information into account. To overcome this shortcoming, in this paper, a new entropy measure, named Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), is proposed to provide stable and reliable results by offering both low-frequency and high-frequency information. Firstly, the F2C signals are created based on the reconstruction of selected wavelet coefficients using wavelet packet decomposition. Then, permutation entropy is used to estimate the complexity and dynamic change of the F2C signals. Experimental analysis is carried out to investigate and compare the performance of the proposed F2CMPE with that of the MPE. Results indicate that the proposed method can give consistent and stable entropy measure for rolling bearing fault diagnosis

Fault Detection of an Internal Combustion Engine through Vibration Analysis by Wavelets Transform

This paper presents a vibration analysis of an internal alternative combustion engine through frequency analysis and wavelet transform, where a form study of the temporary signal and the energy of that signal is carried out to extract certain  characteristic values that allow to differentiate and identify to which pre-established operating conditions, a specific vibration signal belongs. Software is used to make the data decomposition, analysis and value extraction. Different analysis results are presented on this investigation like frequency analysis, spectrogram analysis, wavelet analysis, cross wavelet analysis, and results validation by extracting values of the signals of two tests generating a variation chart showing runs variability if it is big o tiny variability. This analysis is performed to characterize the engine vibration signals so that it is possible to identify an incipient failure in a non-intrusive manner and optimize its maintenance. Also, it can be determined the repetitive form that describes a temporary signal of mechanical vibrations of a motor, if its work cycle it is considered to separate the temporary signal into sections, as long as there are no lower frequency components than the result of dividing the sampling frequency for the number of points that are in a work cycle (the limit frequency)

Fault Diagnosis of Centrifugal Pumps based on the Intrinsic Time-scale Decomposition of Motor Current Signals

Centrifugal pumps are widely used in various manufacturing processes, such as power plants, and chemistry. However, pump problems are responsible for large amount of the maintenance budget. An early detection of such problems would provide timely information to take appropriate preventive actions. This paper investigates the application of Machine Learning Techniques (MLT) in monitoring and diagnosing fault in centrifugal pump. In particular, the focus is on utilising motor current signals since they can be measured remotely for easy and low-cost deployment. Moreover, because the signals are usually produced by a nonlinear process and contaminated by various noises, it is difficult to obtain accurate diagnostic features with conventional signal processing methods such as Fourier spectrum and wavelet transforms as they rely heavily on standard basis functions and often capture limited nonlinear weak fault signatures. Therefore, a data-driven method: Intrinsic Time-scale Decomposition (ITD) is adopted in this study to process motor current signals from different pump fault cases. The results indicate that the proposed ITD technique is an effective method for extracting useful diagnostic information, leading to accurate diagnosis by combining the RMS values of the first Proper Rotation Component (PRC) with the raw signal RMS values

Optimal Wavelet Selection for the Size Estimation of Manufacturing Defects of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria

K

Self-adaptive fault diagnosis of roller bearings using infrared thermal images

Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new self-adaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy are applied respectively to decompose images and seek for the desired decomposition level of the approximation coefficients. After that, the histograms of selected coefficients are used as an input of the feature space selection method by using Genetic Algorithm (GA) and Nearest Neighbor (NN), for the purpose of selecting two salient features that can achieve the highest classification accuracy. The results have demonstrated that the proposed scheme can be employed effectively as an intelligent system for bearing fault diagnosis in rotating machinery

Pembangunan model penentuan keperluan perumahan kajian kes: Johor Bahru, Malaysia

Perumahan merupakan satu komponen penting dalam pembangunan ekonomi di mana ia telah menjadi dasar kerajaan untuk menyediakan rumah bagi setiap rakyat. Rancangan Malaysia terdahulu telah cuba merancang bagi merealisasikan dasar ini. Walaupun anggaran keperluan perumahan dibuat di bawah Rancangan Malaysia, namun anggaran tersebut tidak membayangkan keperluan sebenar pembeli dan penyewa rumah di Malaysia. Negara-negara maju telah menggunakan pelbagai model dalam menentukan keperluan perumahan. Namun begitu, model-model tersebut tidak sesuai digunakan di Malaysia kerana data yang terhad. Kajian ini memfokuskan kepada dua objektif iaitu, mengenal pasti model dan faktor yang signifikan bagi menentukan keperluan perumahan, dan kedua menghasilkan model penentuan keperluan perumahan di Malaysia. Skop kajian ini tertumpu kepada pembeli dan penyewa rumah di Daerah Johor Bahru yang dipilih melalui kaedah pesampelan kelompok pelbagai peringkat. Data diperolehi melalui borang kaji selidik dan dianalisis menggunakan pendekatan kuantitatif. Analisis statistik deskriptif digunakan bagi menghuraikan taburan kekerapan, peratus, min, dan sisihan piawai manakala statistik inferensi iaitu ujian Korelasi Pearson dan Regresi Pelbagai digunakan untuk pembentukan model. Dengan menggunakan kaedah Enter, satu model yang signifikan dapat dihasilkan (F4,178 = 353.699 p < 0.05. Adjusted R square = .886) yang signifikan terhadap dua faktor utama iaitu demografi dan kemampuan. Model yang dihasilkan bagi kajian ini adalah General Linear Model. Model ini dapat digunakan bagi menentukan keperluan perumahan di Johor Bahru. Ia juga berfungsi sebagai alat penting dalam perancangan sektor perumahan pada masa hadapan di Malaysia