A novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser energy operator

Although Ensemble empirical mode decomposition (EEMD) method has been successfully applied to various applications, features extracted using EEMD could not detect anomalies for roller bearings, especially when anomalies includes small defects. In this study a novel feature extraction method is proposed to detect the state of roller bearings. Performance improved EEMD, which is a reliable adaptive method to calculate an appropriate noise amplitude is applied to decompose the acceleration signals into zer0-mean components called intrinsic mode functions (IMFs). Then, three dimensional feature vectors are created by applying the Teager-Kaiser energy operator (TKEO) to the first three IMFs. The novel features obtained from the healthy bearing signals are utilized to construct the separating hyperplane using one-class support vector machine (SVM). In order to validate the method proposed, a number of operating conditions (shaft speed and load) are considered to generate the data (vibration signals) by means of an assembled test rig. It is shown that the proposed method can successfully identify the states of the new samples (healthy and faulty). The uncertainty of the model prediction is investigated computing Margin and the number of support vectors. It create less complex (less fraction of support vectors) and more reliable (higher Margin) hyperplane than the EEMD method

A new bearing fault diagnosis scheme using MED-morphological filter and ridge demodulation analysis

For rolling bearing diagnosis, the major challenge of signal processing technique is to extract the quasi-periodic impulses which generated by rolling bearing fault, especially when rolling bearing operated in the condition of heavy noise. This paper proposed a new bearing fault diagnosis scheme. First, the Minimum Entropy Deconvolution (MED) is taken to obtain the impulse excitations from the bearing vibration signal. Then, two kinds of morphological filter, named average filter(AVG) and difference filter (DIF), are used as the assisted filtering unit to reduce the random noise in original signal and integrate the positive and negative impulse excitations in MED filtered signal, respectively. At last, the STFT based ridge demodulation analysis is applied to the purified signal, and the bearing fault is easily identified by spectral analysis of the demodulated signal. Two simulated signal are analyzed to test the performance of the proposed scheme. In the first case, the periodic impulse signal adding with random noise is analyzed. The result shows that MED-AVG-DIA is the best scheme for impulse feature extraction. In the second case, the pure impulse signal which filtered by MED is analyzed. The result shows that STFT based ridge demodulation analysis can achieve better demodulation effect than other demodulation methods. The proposed fault diagnosis scheme has been further verified by simulation signal and measured vibration signals of defective bearing. The result shown that the proposed scheme is feasible and effective for the fault diagnosis of rolling bearing

A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurem

The Auxiliary Power Unit (APU) is an integral part of an aircraft, providing electrical and pneumatic power to various on-board sub-systems. APU failure results in delay or cancellation of a flight, accompanied by the imposition of hefty fines from the regional authorities. Such inadvertent situations can be avoided by continuously monitoring the health of the system and reporting any incipient fault to the MRO (Maintenance Repair and Overhaul) organization. Generally, enablers for such health monitoring techniques are embedded during a product's design. However, a situation may arise where only the critical components are regularly monitored, and their status presented to the operator. In such cases, efforts can be made during service to incorporate additional health monitoring features using the already installed sensing mechanisms supplemented by maintenance data or by instrumenting the system with appropriate sensors. Due to the inherently critical nature of aircraft systems, it is necessary that instrumentation does not interfere with a system's performance and does not pose any safety concerns. One such method is to install non-intrusive vibroacoustic sensors such that the system integrity is maintained while maximizing system fault diagnostic knowledge. To start such an approach, an in-depth literature survey is necessary as this has not been previously reported in a consolidated manner. Therefore, this paper concentrates on auxiliary power units, their failure modes, maintenance strategies, fault diagnostic methodologies, and their acoustic signature. The recent trend in APU design and requirements, and the need for innovative fault diagnostics techniques and acoustic measurements for future aircraft, have also been summarized. Finally, the paper will highlight the shortcomings found during the survey, the challenges, and prospects, of utilizing sound as a source of diagnostics for aircraft auxiliary power units

Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

Développement d'un modèle de pronostic pour les roulements des éoliennes

RÉSUMÉ: Les climats nordiques font référence aux conditions météorologiques dans lesquelles les éoliennes sont exposées au givrage atmosphérique ou à des températures basses en dehors des limites de conception des éoliennes ce qui cause souvent une réduction de la production énergétique et une augmentation des couts de maintenance. Les conséquences et les coûts liés aux défaillances des composants, sont critiques, car il est difficile d’accéder au site surtout les périodes hivernales et il faut attendre la livraison des pièces de rechange pour remettre l’éolienne en marche et faire face aux défauts. L'échec d'un composant critique, comme une boîte de vitesses, peut endommager d'autres composants et il est donc important d'obtenir un avertissement préalable des problèmes possibles. L’objectif général du projet est d’élaborer une stratégie de maintenance prédictive afin de détecter les pièces et sous-systèmes qui démontrent une tendance à court et à moyen terme vers une anomalie de fonctionnement ou un arrêt complet à cause de bris. Cet objectif général a été atteint, à la fin du projet, par l’atteinte des objectifs spécifiques suivants : Élaboration d’une méthode d’analyse des données issues de capteurs installés sur les composants d’une éolienne permettant la détection d’anomalies d’une pièce ou d’un sous-système. Élaboration d’un modèle de pronostic basé sur les données des capteurs et les réseaux de neurones artificiels capable de calculer la durée de vie utile restante du roulement d’une éolienne. Après la réalisation de l’analyse de criticité des composantes principales de l’éolienne, un seul composant a été retenu dans le cadre de cette étude, vu la nature de données disponibles à analyser. Ce composant est le roulement de la boite de vitesse d’une éolienne. La surveillance d’un ou plusieurs équipements d’une machine peut être vérifiée en analysant, à des périodes de temps bien définies, l’indicateur de dégradation de performance. On retrouve plus d’une technique pour réaliser ces analyses : l’analyse vibratoire, l’analyse des lubrifiants, l’émission acoustique. La vibration est souvent le meilleur indicateur de la santé des machines tournantes, pour ce faire on a abouti dans ce projet à une analyse vibratoire. Finalement, un modèle de pronostic basé sur les réseaux de neurones artificielles a été développé, à partir des signaux générés par les capteurs installés sur les différents sous-systèmes du roulement, avec l’objectif de se doter d’un outil capable de localiser la défaillance sur le roulement et permet la prédiction de sa durée de vie utile restante. Ainsi, d’autres méthodes ont été appliquées telle que la comparaison des données mesurées avec les données prédites numériquement et l’optimisation de l’erreur. -- Mot(s) clé(s) en français : Maintenance prédictive; Éolienne; Roulement; Réseaux de neurones artificiels; pronostic. -- ABSTRACT: Northern climates refer to the weather conditions in which wind turbines are exposed to atmospheric icing or to low temperatures outside the design limits of wind turbines, which often results in reduced energy production and increased maintenance costs. The consequences and costs associated with component failures are critical because it is difficult to access the site especially during winter periods and it is necessary to wait for the delivery of spare parts to restart the wind turbine and deal with defects. Failure of a critical component, such as a gearbox, may damage other components and it is therefore important to obtain prior warning of potential problems. The overall objective of the project is to develop a predictive maintenance strategy to detect parts and subsystems that demonstrate a short- to medium-term trend towards a malfunction or a complete shut down due to breakage. This general objective was achieved at the end of the project by achieving the following specific objectives: Development of a method for analyzing data from sensors installed on the components of a wind turbine allowing the detection of anomaly of a part or of a subsystem. Development of a prognostic model based on sensor data and artificial neural networks capable of calculating the remaining service life of a wind turbine. After carrying out the criticality analysis of the principal components of the wind turbine, only one component was selected for this study, given the nature of available data to be analyzed. This component is the bearing of the gearbox of a wind turbine. Monitoring of one or more equipment of a machine can be verified by analyzing the performance degradation indicator at well-defined time periods. We can find more than one technique for performing these analyzes: vibration analysis, lubricant analysis, acoustic emission, vibration is often the best indicator of the health of rotating machines, to achieve this we have succeeded in this Project to a vibratory analysis. Finally, a prognostic model based on artificial neural networks was developed, based on the signals generated by the sensors installed on the various subsystems of the bearing, with the aim of acquiring a tool capable of locating the Failure and allows the prediction of its remaining service life. Thus, other methods have been applied such as the comparison of the measured data with the numerically predicted data and the optimization of the error. -- Mot(s) clé(s) en anglais : predictive maintenance, wind turbine, bearing, artificial neural network, prognostic

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system