Rolling bearing health assessment using only normal samples

To take maintenance measures timely and to prolong the lifetime of a bearing as a whole, bearing users need to know the current health state of the bearing. Those existing methods for health assessment are mostly based on fault samples and/or decline samples of bearings. However, the bearings are always not allowed to fail in the practical engineering application in consideration of the enormous harms and great damages might be caused by the bearing fault. So the fault samples or decline samples of bearing are often lacking. In this point of view, this paper presents a quantificational health assessment method for rolling bearing based solely on normal samples and SOM network. To demonstrate the capability of the proposed method, a series of vibration datasets of the bearings under various health states were employed to conduct case study. And this paper expresses the uncertainty of assessment results after training for many times by the probability density function (PDF)

Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction

The health state of a rolling bearing keeps changing from a normal state to a slight degradation state followed eventually by a severely degraded state. To make reasonable inspection and maintenance plans, it is necessary to estimate the degradation state and predict the lifetime of a running rolling bearing accurately and in a timely fashion. This paper presents a new method for rolling bearing degradation state estimation and lifetime prediction based on curve similarity recognition. Different from existing methods, this method employs a dynamic time warping algorithm to recognize the curve similarity of those extracted features of rolling bearings in different states of health, which can reflect the intrinsic state of the rolling bearing; it discretizes the bearing degradation state reasonably through curve similarity. Next, the curve similarity is used to train the degradation state estimation model and a support vector machine based lifetime prediction model. Finally, this paper conducts a case study for a rolling bearing with impact degradation and one with wear degradation, respectively. The experimental results indicate that the new proposed method is highly efficient in recognizing the bearing’s degradation state and predicting its lifetime

Approach to health monitoring and assessment of rolling bearing

A bearing is the most common and vital element in the majority of rotating machinery. Condition monitoring and performance assessment of rolling bearing have recently attracted significant attention. This paper proposes a set of methodologies to realize the efficient health monitoring and assessment of rolling bearing. Considering the difficulties and disadvantages in detecting the fault signal of rolling bearing with background noise, this paper presents a method based on the Duffing oscillator and Hu’s moment invariant for health monitoring. The proposed method mainly combines the chaotic oscillator and moment invariant, fully utilizing the sensitivity of the former to detect the fault signal and taking the latter as a quantitative index for fault identification without the need for a qualitative artificial judgment on the Duffing oscillator phase trajectory map. To provide the optimal performance of Hu’s moment invariant in automatic recognition for the phase trajectory map, the influencing principle of different oscillator parameters was analyzed. Therefore, the health state of rolling bearing can be automatically monitored by quantitatively identifying the transition state of the phase trajectory map. A health assessment model was established to evaluate the health state of bearings. Wavelet packet transform was used to extract the features (approximate entropy) of bearing vibration signal, which were input into the self-organizing map (SOM) network. The health state of rolling bearings was then assessed using the SOM network and confidence values. A case study on health monitoring and assessment for rolling bearing was conducted to demonstrate the effectiveness and accuracy of the proposed methods

Rolling bearing fault diagnosis based on health baseline method

In order to excavate the relationship between the different features of the vibration signal, and to provide more useful information for the fault diagnosis of rolling bearings, this paper developed a new method of fault diagnosis-health baseline method and introduced the technological process of this method in detail. Through the case study, a health baseline based on two kinds of linear models was constructed. After testing, this method can distinguish the normal state of the rolling bearing, the external ring fault and the rolling element fault, which indicates that the method was feasible and effective for the fault diagnosis of the rolling bearing

Performance assessment of hydraulic servo system based on bi-step neural network and autoregressive model

In recent years, condition monitoring and fault diagnosis of hydraulic servo systems has attracted increasing attention. However, few studies have focused on the performance assessment of these systems. This study proposes a performance assessment method based on a bi-step neural network and an autoregressive model for a hydraulic servo system; the performance is quantized by the performance confidence value (CV). First, a fault observer based on a radial basis function (RBF) neural network is designed to estimate the output of the system and calculate the residual error. Second, the corresponding adaptive threshold is generated by using another RBF neural network during system operation. Third, the difference value between the coefficients of the autoregressive model for the generated residual error and the adaptive threshold is obtained, and the Mahalanobis distance (MD) between the most recent difference (unknown conditions) and the constructed Mahalanobis space by using samples under normal conditions is calculated. Then, the condition of the system can be determined by normalizing the MD into a CV. The proposed method was further validated for three types of faults, and data were obtained using a simulation model. The experimental analysis results show that the performance of hydraulic servo systems can be assessed effectively by the proposed method

Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries based on artificial fish swarm algorithm (AFSA) and particle filter (PF), which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise

Bearing health assessment based on chaotic characteristics

Abstract. Vibration signals extracted from rotating parts of machinery carry a lot of useful information about the condition of operating machine. Due to the strong non-linear, complex and non-stationary characteristics of vibration signals from working bearings, an accurate and reliable health assessment method for bearing is necessary. This paper proposes to utilize the selected chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map (SOM). Both Grassberger-Procaccia algorithm and Takens' theory are employed to calculate the characteristic vector which includes three chaotic characteristics, such as correlation dimension, largest Lyapunov exponent and Kolmogorov entropy. After that, SOM is used to map the three corresponding characteristics into a confidence value (CV) which represents the health state of the bearing. Finally, a case study based on vibration datasets of a group of testing bearings was conducted to demonstrate that the proposed method can reliably assess the health state of bearing

Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition

This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using non-subsampled contourlet transform (NSCT). NSCT imitates the multichannel characteristic of the human visual system (HVS) that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing

TF Entropy and RFE Based Diagnosis for Centrifugal Pumps Subject to the Limitation of Failure Samples

In practical engineering, the vibration-based fault diagnosis with few failure samples is gaining more and more attention from researchers, since it is generally hard to collect sufficient failure records of centrifugal pumps. In such circumstances, effective feature extraction becomes quite vital, since there may not be enough failure data to train an end-to-end classifier, like the deep neural network (DNN). Among the feature extraction, the entropy combined with signal decomposition algorithms is a powerful choice for fault diagnosis of rotating machinery, where the latter decomposes the non-stationary signal into multiple sequences and the former further measures their nonlinear characteristics. However, the existing entropy generally aims at processing the 1D sequence, which means that it cannot simultaneously extract the fault-related information from both the time and frequency domains. Once the sequence is not strictly stationary (hard to achieve in practices), the useful information will be inevitably lost due to the ignored domain, thus limiting its performance. To solve the above issue, a novel entropy method called time-frequency entropy (TfEn) is proposed to jointly measure the complexity and dynamic changes, by taking into account nonlinear behaviors of sequences from both dimensions of time and frequency, which can still fully extract the intrinsic fault features even if the sequence is not strictly stationary. Successively, in order to eliminate the redundant components and further improve the diagnostic accuracy, recursive feature elimination (RFE) is applied to select the optimal features, which has better interpretability and performance, with the help of the supervised embedding mechanism. To sum up, we propose a novel two-stage method to construct the fault representation for centrifugal pumps, which develops from the TfEn-based feature extraction and RFE-based feature selection. The experimental results using the real vibration data of centrifugal pumps show that, with extremely few failure samples, the proposed method respectively improves the average classification accuracy by 12.95% and 33.27%, compared with the mainstream entropy-based methods and the DNN-based ones, which reveals the advantage of our methodology