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

Planetary gearboxes performance degradation analysis and prediction

Gearbox is the core component of various machines and vehicles, so it is necessary to monitor the performance degradation of gearbox for improving the reliability of mechanical equipment and vehicle. The use of the health condition monitoring on core components such as gearbox can reduce the economic losses due to its failure In this paper, some parameters are extracted from data of run-to-failure test, for example, kurtosis, RMS and energy. The performance degradation of the gearbox is analyzed and predicted by the monitoring the parameters. In this paper, some forecasting methods such as ARMA model, moving average line and feed forward neural network are compared by calculate absolute error

Planetary gearbox fault diagnosis based on pseudo-fault signal assisted EMD

Pseudo-fault signal assisted empirical mode decomposition (PFS-EMD) is put forward for fault detection and isolation, which is built upon EMD, envelope analysis and pseudo-fault signal. And this method has been proved that it is usable in fault diagnosis of simple gear pair. However, this method is still need further study that whether it can be used for fault diagnosis of complex gearbox. Moreover, planetary gearbox fault diagnosis is much more complicated than fixed-axis gearbox. This paper makes a further study of PFS-EMD and focuses on its application. Combining with the seeded failure experimental data of planetary gearbox, this paper show that PFS-EMD can enhance fault information and weaken other useless information in complex gearbox fault diagnosis

Inspection period determination for two-stage degraded system

At present studies on degradation process are mainly single stage degradation mode, however, in practice the system degradation process is generally multi-stage. Based on general degradation process modeling, the paper assumed degenerate distribution of two-stage mode obey various normal distribution, shock times obey Poisson process. Reliability modeling and mean time to failure modeling of two-stage degraded mode are studied. Functional check period determination methods are used to calculate inspection periods for different degradation stage. In numerical example, inspection periods for system with two-stage degradation process are analyzed

Planetary gearbox remaining useful life estimation based on state space model

As planetary gearboxes are widely used in various kinds of engineering, the fault diagnosis and prognosis of planetary gearbox is very important. This paper proposes a remaining useful life estimation method based on state space model. The degradation process is assumed to be Gamma distribution. And experience maximization method and particle filter is used to estimate the parameters of state space model. A planetary gearbox life-cycle experiment is done to obtain the degradation data and verify the effectiveness of the proposed method

Rolling bearing fault diagnosis using modified K-means cluster analysis

Rolling bearing is essential component of most rotating machinery, fault diagnosis of rolling bearing is significant for enhance the reliability of mechanical device. It is becoming a hot research topic recent years. There are some disadvantages for existing methods, like computing complex, long spending time and so on. In order to overcome these shortcomings of existing methods, this paper present a modified K-means cluster analysis which is used to bearing fault diagnostics. And the data of Case Western Reserve University are used to validate effectiveness of the proposed method

Free gas accumulations in basal shear zones of mass-transport deposits (Pearl River Mouth Basin, South China Sea): An important geohazard on continental slope basins

Free gas is an important trigger of instability on continental slopes, and resulting mass-wasting strata can potentially form competent seals to hydrocarbon accumulations. This work uses two high-quality 3D seismic volumes to investigate fluid accumulations at the base of mass-transport deposits in the Pearl River Mouth Basin, South China Sea. In parallel, IODP/ODP borehole data are used to document the petrophysical character of mass-transport deposits formed in similar continental-slope environments to the South China Sea. The interpreted data show gas accumulations as comprising enhanced seismic reflections that are discordant, or vertically stacked, below mass-transport deposits with chaotic seismic facies. Gas was accumulated in basal shear zones of mass-transport deposits in response to differences in capillary pressure and porosity. Free gas in Zone A covers an area of at least 18 km2. In Zone B, the free gas is sub-circular in plan view and covers an area of 30.58 km2 for a volume of sediment approaching 1.5 km3. This work is important as it shows that vertical migration of gas is not significant in mass-transport deposits from the Pearl River Mouth Basin, but up-dip migration along their basal shear zones is suggested in multiple locations. As a result, free gas can pinch-out laterally to extend 1–2 km beyond these same basal shear zones. As a corollary, we show that free gas accumulations below mass-transport deposits comprise an important geohazard and should be taken into account when drilling continental-slope successions in both the South China Sea and continental margins recording important mass wasting. Strata charged with free gas form weak layers, hinting at a novel trigger of retrogressive slope failures on continental slopes worldwide

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

Application of empirical mode decomposition and Euclidean distance technique for feature selection and fault diagnosis of planetary gearbox

Planetary gearbox plays an important role in large and complex mechanical equipment due to the advantage that it can provide larger transmission ratio in a compact space than fixed shaft gearbox. However, its fault diagnosis is a dilemma due to the special structure and harsh working conditions. This paper applies Empirical Mode Decomposition (EMD) and Euclidean Distance Technique (EDT) for planetary gearbox feature selection and fault diagnosis. EMD is a self-adaptive signal processing method that can be applied to non-linear and non-stationary signal and it can also get the aim of de-noising. EDT can give out the quantitative fault diagnosis result. And its theoretical knowledge is easy to understand. An intrinsic mode function (IMF) selection method based on energy ratio is proposed to select IMFs which include sensitive fault information. A two-stage feature selection and weighting method based on EDT is applied to get a new combinative feature and 36 feature parameters are extracted before this process. Then, the feature vector matrix of each raw signal can be computed out by extracting the new combinative feature from every IMF. Finally, the diagnosis result can be obtained through calculating the Euclidean Distance value between two feature vector matrixes. Namely, the health state of the tested signal is as same as the trained signal which the Euclidean Distance between them is the minimum. The performance of the proposed method is validated by experimental data and industrial data