Maintenance policy for two-stage deteriorating mode system based on cumulative damage model

For the system degradation process undergoing a sudden change, optimal maintenance policies were developed using the cumulative damage model and two-stage degradation modeling. Single shock damage value and the number of shock times are assumed to be normal distribution and homogeneous Poisson process, respectively. On this basis, average long-run cost rate of a renewal cycle was modeled with considering the probabilities of corrective, preventive and continuous monitoring, respectively. In order to develop an optimal policy, four types of maintenance policies (i.e., global, time-depended, adaptive and simplified adaptive policies) were analyzed with different alarm thresholds and inter-inspection time. Influence analysis of different parameters for maintenance policy was given, where different maintenance policies were compared in terms of average long-run cost rate. In addition, the impacts of degradation model parameters (i.e., change-point distribution, shock strength, shock frequency) on the average long-run cost rate were analyzed. Finally, maintenance policy for gearbox degradation experiment was analyzed in case study

Gear fault diagnosis and damage level identification based on Hilbert transform and Euclidean distance technique

This paper deals with the problem of gear fault diagnosis with multiple possible fault modes and damage levels. Gears are the most essential parts in rotating machinery. Their health status is a significant index to indicate whether machines can run continually or not. So, gear fault diagnosis and damage level identification is very important in engineering practice. An accuracy way to identify the state of gears is urgently needed for the maintenance decision making. In this paper, a novel gear fault diagnosis and damage level identification method based on Hilbert transform (HT) and Euclidean distance technique (EDT) is developed. The energies of six frequency bands are used as the fault feature through the contrast with other two parameters, kurtosis and skewness. Then HT is used to obtain analytic signal. Finally, EDT is utilized to recognize the different fault modes and damage levels. This method is implemented by two stages, i.e., classifying different fault modes and identifying damage levels for every fault mode. The effectiveness of this methodology is demonstrated by compare to fisher discriminant analysis (FDA) using experiment data acquired from a real gearbox. In addition, industrial data is also used to validate the effectiveness of the proposed method

An offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter

Planetary gearbox is increasingly used in many kinds of rotary machinery in recent years. Due to the specialty of its structure, fault diagnosis for planetary gearbox is very difficult compared with the fixed shaft gearbox. This paper proposed an offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter. Firstly, the framework of the method proposed in this paper was introduced. Then, experimental data and industrial data were utilized to validate the effectiveness of the method. And the combination of empirical mode decomposition and adaptive multi-scale morphological dilation-erosion gradient filter was found very suitable to be used in the planetary gearbox fault diagnosis compared with other five filters. The proposed method was demonstrated to be of good performance on both extracting faults characteristic frequency and de-noising

A novel faults detection method for rolling bearing based on RCMDE and ISVM

The rolling bearing is an essential element widely used in the rotating machinery. Bearing failures are among the main reasons for breakdown of rotating machinery. Therefore, fault detection of bearing is necessary to reduce the probability of breakdown and safety accidents. A novel fault diagnosis method for rolling bearing based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Improved Support Vector Machine (ISVM) is presented in this paper. The RCMDE is a new irregular index in biomedical signal analysis, which has lower computational cost and more stable results. Therefore, the RCMDE is introduced as fault feature to represent the bearing fault characteristics. After feature extraction, an improved support vector machine based on whale optimization algorithm (WOA) and support vector machine (SVM) is proposed as a fault classifier, which has the advantages of less training samples and good classification effect. The effectiveness of the proposed method in bearing fault diagnosis is verified by using bearing fault experimental data

A novel method for detecting bearing defects based on EMD and fractal dimension

Bearings are widely used in rotating machines. Its health status is a significant index to indicate whether machines run continually or not. Detecting the bearing defects timely is very important for the maintenance decision making. In this paper, a novel bearing defects detection method based on EMD and Fractal Dimension is developed. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EMD, and the fractal dimension of IMFs which contains bearing fault characteristic information are calculated, and these characteristic parameters are used to identify bearing fault types. The effectiveness of this methodology is demonstrated using experimental data

Bearing prognostics with non-trendable behavior based on shock pulse method and frequency analysis

Bearings are one of the most important parts of rotating machineries. Their fault diagnosis and prognosis are critical for the maintenance decision making. In reality, few bearings are working under constant operating conditions. So, robust features which are not sensitive to the operating condition are needed for bearing prognostics. Sometimes, even if they are working under stationary conditions, common-used degradation features are non-trendable and cannot be used to predict the remaining useful lives. In order to address these two issues, shock pulse method and frequency analysis are combined to detect the incipient fault and predict the remaining useful lives. Maximum normalized shock value which is extracted using shock pulse method can reflect the degradation process more robust under non-stationary conditions. And frequency analysis can identify the change points of degradation states when degradation features are non-trendable. Finally, a case study is conducted where the proposed methods are demonstrated by analyzing the 2012 PHM challenge data sets

The enhancement of bearing fault detection using narrowband interference cancellation

Bearings play an important role in mechanical transmission. Many disasters are due to bearing faults. This has driven the need in research for early bearing fault detection. The goal is to extract the periodic impulse signals from the very noise signal which are indicative of a bearing fault. This is done by enhancing impulsive signals while suppressing other signals. This paper used a new method, Narrowband Interference Cancellation, to detect incipient bearing fault. This method filters the narrowband signal not associated with the impulsive signal produced by bearing fault. This improves the signal to noise ratio of impulse train associated with the bearing fault frequency. Finally this methodology is demonstrated on a bearing outer race fault

An offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter

Planetary gearbox is increasingly used in many kinds of rotary machinery in recent years. Due to the specialty of its structure, fault diagnosis for planetary gearbox is very difficult compared with the fixed shaft gearbox. This paper proposed an offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter. Firstly, the framework of the method proposed in this paper was introduced. Then, experimental data and industrial data were utilized to validate the effectiveness of the method. And the combination of empirical mode decomposition and adaptive multi-scale morphological dilation-erosion gradient filter was found very suitable to be used in the planetary gearbox fault diagnosis compared with other five filters. The proposed method was demonstrated to be of good performance on both extracting faults characteristic frequency and de-noising

Gear Crack Level Classification Based on EMD and EDT

Gears are the most essential parts in rotating machinery. Crack fault is one of damage modes most frequently occurring in gears. So, this paper deals with the problem of different crack levels classification. The proposed method is mainly based on empirical mode decomposition (EMD) and Euclidean distance technique (EDT). First, vibration signal acquired by accelerometer is processed by EMD and intrinsic mode functions (IMFs) are obtained. Then, a correlation coefficient based method is proposed to select the sensitive IMFs which contain main gear fault information. And energy of these IMFs is chosen as the fault feature by comparing with kurtosis and skewness. Finally, Euclidean distances between test sample and four classes trained samples are calculated, and on this basis, fault level classification of the test sample can be made. The proposed approach is tested and validated through a gearbox experiment, in which four crack levels and three kinds of loads are utilized. The results show that the proposed method has high accuracy rates in classifying different crack levels and may be adaptive to different conditions

An offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter

Planetary gearbox is increasingly used in many kinds of rotary machinery in recent years. Due to the specialty of its structure, fault diagnosis for planetary gearbox is very difficult compared with the fixed shaft gearbox. This paper proposed an offline fault diagnosis method for planetary gearbox based on empirical mode decomposition and adaptive multi-scale morphological gradient filter. Firstly, the framework of the method proposed in this paper was introduced. Then, experimental data and industrial data were utilized to validate the effectiveness of the method. And the combination of empirical mode decomposition and adaptive multi-scale morphological dilation-erosion gradient filter was found very suitable to be used in the planetary gearbox fault diagnosis compared with other five filters. The proposed method was demonstrated to be of good performance on both extracting faults characteristic frequency and de-noising