Zhixin Yang

Real-time fault diagnosis for gas turbine generator systems using extreme learning machine

Real-time fault diagnostic system is very important to maintain the operation of the gas turbine generator system (GTGS) in power plants, where any abnormal situation will interrupt the electricity supply. The GTGS is complicated and has many types of component faults. To prevent from interruption of electricity supply, a reliable and quick response framework for real-time fault diagnosis of the GTGS is necessary. As the architecture and the learning algorithm of extreme learning machine (ELM) are simple and effective respectively, ELM can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). This paper therefore proposes a new application of ELM for building a real-time fault diagnostic system in which data pre-processing techniques are integrated. In terms of data pre-processing, wavelet packet transform and time-domain statistical features are proposed for extraction of vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features in order to shorten the fault identification time and improve accuracy. To evaluate the system performance, a comparison between ELM and the prevailing SVM on the fault detection was conducted. Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy. This paper is also the first in the literature that explores the superiority of the fault identification time of ELM.

Fast detection of impact location using kernel extreme learning machine

Abstract Damage location detection has direct relationship with the field of aerospace structure as the detection system can inspect any exterior damage that may affect the operations of the equipment. In the literature, several kinds of learning algorithms have been applied in this field to construct the detection system and some of them gave good results. However, most learning algorithms are time-consuming due to their computational complexity so that the real-time requirement in many practical applications cannot be fulfilled. Kernel extreme learning machine (kernel ELM) is a learning algorithm, which has good prediction performance while maintaining extremely fast learning speed. Kernel ELM is originally applied to this research to predict the location of impact event on a clamped aluminum plate that simulates the shell of aerospace structures. The results were compared with several previous work, including support vector machine (SVM), and conventional back-propagation neural networks (BPNN). The comparison result reveals the effectiveness of kernel ELM for impact detection, showing that kernel ELM has comparable accuracy to SVM but much faster speed on current application than SVM and BPNN.

RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM

Manufacturing execution systems (MES) have recently been introduced to monitor various manufacturing objects (MOs) in dynamic shop floors; they can leverage the efficiency of information flow across functional layers for planning and control. However, current MES practices using traditional indoor positioning algorithms face several difficulties in tracking MOs for wireless manufacturing: (1) inefficient wireless data acquisition in the shop-floor environment, (2) lack of a reliable and accurate real-time signal processing method for handling massive signal data, and (3) the positions of reference objects cannot be treated as in a static environment when unknown manufacturing orders arrive in a streaming manner. This paper proposes to handle the first challenge by adopting RFID technology that can constantly capture the wireless signals sent from tags mounted on various MOs. The second difficulty can be solved by applying the online sequential extreme learning machine (OS-ELM) that inherits the elegant properties of ELM in terms of extremely fast learning speed and high generalization performance. The OS-ELM based positioning method also addresses the third issue in which an online localization model has been constructed in a streaming manner. The proposed method can greatly reduce the training time without costly retraining of the previously trained data together with the newly arrived data. With the novel OS-ELM based RFID positioning framework, the MOs are upgraded to smart manufacturing objects (SMOs), and the processes are enhanced with real-time signal processing and intelligent tracking capabilities. The experimental results verify that the proposed positioning method is superior to other state-of-the-art algorithms in terms of accuracy, efficiency, and robustness.

A Hybrid EEMD-Based SampEn and SVD for Acoustic Signal Processing and Fault Diagnosis

Acoustic signals are an ideal source of diagnosis data thanks to their intrinsic non-directional coverage, sensitivity to incipient defects, and insensitivity to structural resonance characteristics. However this makes prevailing signal de-nosing and feature extraction methods suffer from high computational cost, low signal to noise ratio (S/N), and difficulty to extract the compound acoustic emissions for various failure types. To address these challenges, we propose a hybrid signal processing technique to depict the embedded signal using generally effective features. The ensemble empirical mode decomposition (EEMD) is adopted as the fundamental pre-processor, which is integrated with the sample entropy (SampEn), singular value decomposition (SVD), and statistic feature processing (SFP) methods. The SampEn and SVD are identified as the condition indicators for periodical and irregular signals, respectively. Moreover, such a hybrid module is self-adaptive and robust to different signals, which ensures the generality of its performance. The hybrid signal processor is further integrated with a probabilistic classifier, pairwise-coupled relevance vector machine (PCRVM), to construct a new fault diagnosis system. Experimental verifications for industrial equipment show that the proposed diagnostic system is superior to prior methods in computational efficiency and the capability of simultaneously processing non-stationary and nonlinear condition monitoring signals.

Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis

The automotive engine is prone to various faults due to its complex structure and running conditions. Development of a fast response and accurate intelligent system for fault diagnosis of automotive engines is therefore greatly urged. The engine fault diagnosis faces challenges because of the existence of simultaneous-faults which are multiple single-faults appear concurrently. Another challenge is the high cost in acquiring the exponentially increased simultaneous-fault signals. Traditional signal-based engine fault diagnostic systems may not give reliable diagnostic results because they usually rely on single classifier and one kind of engine signal. To enhance the reliability of fault diagnosis and the number of detectable faults, this research proposes a new diagnostic framework namely probabilistic committee machine (PCM). The framework combines feature extraction (empirical mode decomposition and sample entropy), a parameter optimization algorithm, and multiple sparse Bayesian extreme learning machines (SBELM) to form an intelligent diagnostic framework. Multiple SBELM networks are built to form different domain committee members. The members are individually trained using air ratio, ignition pattern and engine sound signals. The diagnostic result from each fault detection member is then combined by using a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifier. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single and simultaneous-faults for automotive engines while the system is trained by single-fault patterns only.

Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

Machine condition monitoring and fault diagnosis based on support vector machine

Due to the importance of rotating machinery as one of the most widely used industrial element, development a proper monitoring and fault diagnosis technique to prevent malfunction and failure of machine during operation is necessary. This paper presents a method for gearbox fault diagnosis based on feature extraction technique, distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, the features of raw data are extracted through the wavelet packet transform (WPT) and time-domain statistical features. Secondly, the compensation distance evaluation technique is applied to select optimal feature via sensitivities ranking. Finally, the optimal features are input into the SVMs to identify different faults. The diagnosis result shows that the SVMs ensemble is able to reliable recognize not only different faults styles and severities but also the compound faults in high accurate rate.

Gearbox fault diagnosis based on artificial neural network and genetic algorithms

System maintenance for reliable running of key machinery is critical to many industries, where condition monitoring and fault diagnosis is important supporting technology. This paper selects a typical component in rotating machinery, the gearbox, as the target to study a proper monitoring and fault diagnosis method to prevent malfunction and failure. The failure is divided into two levels. One is at the component level that includes various gear faults, and another is at system level that studies machinery statuses include looseness, misalignment and unbalance. A prototype system is built for experiment. Two intelligent methods include artificial neural network (ANN) and genetic algorithms (GAs) are combined to carry out signal classification and analysis. ANNs are one of the common machine learning technologies that used for detecting and diagnosing faults in rotating machinery. To look for a feasible combined solution, this paper tests the effect of back-propagation (BP) network and GAs are used in this paper for selecting the significant input features in a large set of possible features in machine condition monitoring with vibration signals. Considering the performance of machine learning system are hard to predict, and the quality of input signal is a major factor affecting the performance of training and learning of the system itself. Signal preprocessing is executed through feature extraction by wavelet packet transforms (WPT) technology and time domains statistical analysis to generate statistic variables for analysis. With an aim to identify a proper diagnosis approach, the effect of BP network and GAs are verified with case studies.

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.

A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.