Preventive maintenance optimization policy based on a three-stage failure process in finite time horizon

In this paper, a preventive maintenance optimization policy based on a three-stage failure process in finite time horizon is proposed. The lifetime of system is divided into three stages by the concept of three-stage failure process, which is corresponding to the three color scheme commonly used in industry. The subsequent inspection interval is halved when the minor defective stage is identified. Once identifying the severe defective stage, maintenance action is carried out. A numerical example is presented to demonstrate the efficiency of the proposed models

A joint optimal policy of inspection and age based replacement based on a three-stage failure process

Preventive maintenance (PM) and condition-based maintenance (CBM) are two dominant maintenance policies in industrial applications. Inspection activities are the foundation of PM and CBM policies as to provide the operating information of system through processing the collected vibration data. Age based replacement is one of the most used preventive maintenance policy aiming at avoiding unplanned downtime and higher failure loss. This paper proposes a joint optimal policy of inspection and age based replacement based on a three-stage failure process for a single component system. The three-stage failure process, which is closer to reality, divides the failure process of system into three stages: namely normal, minor defective and severe defective. When the severe defective stage is identified, maintenance action is carried out immediately. The system is replaced once it reaches certain age. However, two potential actions are considered and analyzed in this paper when the minor defective stage is identified: halving the subsequent inspection interval or replacing the item immediately. As inspection may not be perfect because of the complexity of plant items, both perfect and imperfect inspection cases are considered. Finally, a case study is presented to demonstrate the efficiency of the proposed models

An adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection

Bearing is widely used in the rotating machinery and prone to failure due to the harsh working environment. The bearing fault-induced impulses are weak because of poor background noise, long vibration transmission path, and slight fault degree. Therefore, the bearing fault detection is difficult. A novel adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection is proposed. Stochastic resonance (SR) is like a nonlinear filter, which can enhance the weak fault-induced impulses while suppressing the noise. However, the parameters of the nonlinear system exert an influence on the SR effect, and the optimal parameters are difficult to be found. Multi-agent cuckoo search (MACS) algorithm is an excellent heuristic optimization algorithm and can be used to search the parameters of nonlinear system adaptively. Two bearing fault signals are used to validate the effectiveness of our proposed method. Three other adaptive SR methods based on cuckoo search algorithm, particle swarm optimization or genetic algorithm are also used for comparison. The results show that MACS can find the optimal parameters more quickly and more accurately, and our proposed method can enhance the fault-induced impulses efficiently

An availability model based on a three-stage failure process under age based replacement

This paper proposes a joint optimal policy of inspection and age based replacement based on a three-stage failure process to jointly optimize the inspection and replacement intervals. The three-stage failure process divides the failure process of system into three stages: namely normal, minor defective and severe defective. When the minor defective stage is identified, the subsequent inspection interval is halved. Once identifying the severe defective stage, the maintenance action is carried out immediately. The system is replaced once it reaches the certain age. Finally, a numerical example is presented to demonstrate the efficiency of the proposed model

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

Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution

Detecting periodic impulse signal (PIS) is the core of bearing fault diagnosis. Earlier fault detected, earlier maintenance actions can be implemented. On the other hand, remaining useful life (RUL) prediction provides important information when the maintenance should be conducted. However, good degradation features are the prerequisite for effective RUL prediction. Therefore, this paper mainly concerns earlier fault detection and degradation feature extraction for bearing. Maximum correlated kurtosis deconvolution (MCKD) can enhance PIS produced by bearing fault. Whereas, it only achieve good effect when bearing has severe fault. On the contrary, PIS produced by bearing weak fault is always masked by heavy noise and cannot be enhanced by MCKD. In order to resolve this problem, a revised empirical mode decomposition (EMD) algorithm is used to denoise bearing fault signal before MCKD processing. In revised EMD algorithm, a new recovering algorithm is used to resolve mode mixing problem existed in traditional EMD and it is superior to ensemble EMD. For degradation analysis, correlated kurtosis (CK) value is used as degradation indicator to reflect health condition of bearing. Except of theory analysis, simulated bearing fault data, injected bearing fault data, real bearing fault data and bearing degradation data are used to verify the proposed method. Simulated bearing fault data is used to explain the existed problems. Then, injected bearing fault data and real bearing fault data are used to demonstrate the effectiveness of proposed method for fault diagnosis. Finally, bearing degradation data is used to verify the degradation feature CK extracted based on proposed method. All these case studies show the effectiveness of proposed fault diagnosis and degradation tracking method

Application of an improved Levenberg-Marquardt back propagation neural network to gear fault level identification

Chip fault is one of the most frequently occurring damage modes in gears. Identifying different chip levels, especially for incipient chip is a challenge work in gear fault analysis. In order to classify the different gear chip levels automatically and accurately, this paper developed a fast and accurate method. In this method, features which are specially designed for gear damage detection are extracted based on a revised time synchronous averaging algorithm to character the gear conditions. Then, a modified Levenberg-Marquardt training back propagation neural network is utilized to identify the gear chip levels. In this modified neural network, damping factor and dynamic momentum are optimized simultaneously. Fisher iris data which is the machine learning public data is used to validate the performance of the improved neural network. Gear chip fault experiments were conducted and vibration signals were captured under different loads and motor speeds. Finally, the proposed methods are applied to identify the gear chip levels. The classification results of public data and gear chip fault experiment data both demonstrated that the improved neural network gets a better performance in accuracy and speed compared to the neural networks which are trained by El-Alfy’s and Norgaard’s Levenberg-Marquardt algorithm. Therefore, the proposed method is more suitable for on-line condition monitoring and fault diagnosis

Application of an improved Levenberg-Marquardt back propagation neural network to gear fault level identification

Chip fault is one of the most frequently occurring damage modes in gears. Identifying different chip levels, especially for incipient chip is a challenge work in gear fault analysis. In order to classify the different gear chip levels automatically and accurately, this paper developed a fast and accurate method. In this method, features which are specially designed for gear damage detection are extracted based on a revised time synchronous averaging algorithm to character the gear conditions. Then, a modified Levenberg-Marquardt training back propagation neural network is utilized to identify the gear chip levels. In this modified neural network, damping factor and dynamic momentum are optimized simultaneously. Fisher iris data which is the machine learning public data is used to validate the performance of the improved neural network. Gear chip fault experiments were conducted and vibration signals were captured under different loads and motor speeds. Finally, the proposed methods are applied to identify the gear chip levels. The classification results of public data and gear chip fault experiment data both demonstrated that the improved neural network gets a better performance in accuracy and speed compared to the neural networks which are trained by El-Alfy’s and Norgaard’s Levenberg-Marquardt algorithm. Therefore, the proposed method is more suitable for on-line condition monitoring and fault diagnosis

Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine

Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value

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