Qiang Miao

Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

Abstract Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of a system. It gives operators information about when the component should be replaced. In recent years, a lot of research has been conducted on battery reliability and prognosis, especially the remaining useful life prediction of the lithium-ion batteries. Particle filter (PF) is an effective method for sequential signal processing. It has been used in many areas, including computer vision, target tracking, and robotics. However, the accuracy of the PF is not high. This paper introduces an improved PF algorithm-unscented particle filter (UPF) into the battery remaining useful life prediction. First, PF algorithm and UPF algorithm are described separately. Then, a degradation model is built based on the understanding of lithium-ion batteries. Finally, the prediction results can be obtained using the degradation model and the UPF algorithms. According to the analysis results, it can be seen that UPF can predict the actual RUL with an error less than 5%.

Health Assessment of Cooling Fan Bearings Using Wavelet-Based Filtering

As commonly used forced convection air cooling devices in electronics, cooling fans are crucial for guaranteeing the reliability of electronic systems. In a cooling fan assembly, fan bearing failure is a major failure mode that causes excessive vibration, noise, reduction in rotation speed, locked rotor, failure to start, and other problems; therefore, it is necessary to conduct research on the health assessment of cooling fan bearings. This paper presents a vibration-based fan bearing health evaluation method using comblet filtering and exponentially weighted moving average. A new health condition indicator (HCI) for fan bearing degradation assessment is proposed. In order to collect the vibration data for validation of the proposed method, a cooling fan accelerated life test was conducted to simulate the lubricant starvation of fan bearings. A comparison between the proposed method and methods in previous studies (i.e., root mean square, kurtosis, and fault growth parameter) was carried out to assess the performance of the HCI. The analysis results suggest that the HCI can identify incipient fan bearing failures and describe the bearing degradation process. Overall, the work presented in this paper provides a promising method for fan bearing health evaluation and prognosis.

Online Anomaly Detection for Hard Disk Drives Based on Mahalanobis Distance

A hard disk drive (HDD) failure may cause serious data loss and catastrophic consequences. Online health monitoring provides information about the degradation trend of the HDD, and hence the early warning of failures, which gives us a chance to save the data. This paper developed an approach for HDD anomaly detection using Mahalanobis distance (MD). Critical parameters were selected using failure modes, mechanisms, and effects analysis (FMMEA), and the minimum redundancy maximum relevance (mRMR) method. A self-monitoring, analysis, and reporting technology (SMART) data set is used to evaluate the performance of the developed approach. The result shows that about 67% of the anomalies of failed drives can be detected with zero false alarm rate, and most of them can provide users with at least 20 hours during which to backup the data.

Prognostics and health monitoring for lithium-ion battery

Health monitoring is used to analyze and predict the battery health status. However, no matter what health monitoring methods and parameters are, a major aim is to improve the battery reliability through surveillance and prognostics. Hence, the latest known methods of state estimation and life prediction based on battery health monitoring are discussed in this paper. Through comparing their characteristics respectively, a prognostics-based fusion technique is proposed that combines physics-of-failure (PoF) with data-driven technology. The fusion approach not only investigates battery failure mechanism caused by environmental and internal characteristics, but also assesses parameters with aid of real-time health monitoring. The specific method is presented to realize the estimation on remaining useful life (RUL) of batteries.

Identification of multiple characteristic components with high accuracy and resolution using the zoom interpolated discrete Fourier transform

Complex systems can significantly benefit from condition monitoring and diagnosis to optimize operational availability and safety. However, for most complex systems, multi-fault diagnosis is a challenging issue, as fault-related components are often too close in the frequency domain to be easily identified. In this paper, the interpolated discrete Fourier transform (IpDFT) with maximum sidelobe decay windows is investigated for machinery fault feature identification. A novel identification method called the zoom IpDFT is proposed, which combines the idea of local frequency band zooming-in with the IpDFT and demonstrates high accuracy and frequency resolution in signal parameter estimation when different characteristic frequencies are very close. Simulation and a case study on rolling element bearing vibration data indicate that the proposed zoom IpDFT based on multiple modulations has better capability to identify characteristic components than do traditional methods, including fast Fourier transform (FFT) and zoom FFT.

Health monitoring of hard disk drive based on Mahalanobis distance

A hard disk drive (HDD) is one of the core components of most computer systems. A failure of HDD may cause serious data loss and catastrophic consequences. Thus, health monitoring and anomaly prediction for HDD are critical to prevent data loss and make strategies for data backup. This paper analyzed the potential failure modes and failure mechanisms influencing on HDD reliability by FMMEA (Failure Modes, Mechanisms and Effects Analysis) method and performed the prioritization by estimating the risk priority numbers. The Head Disk Interface (HDI) and head stack assembly related failure and relevant performance parameters are identified as the dominant failure mode and health monitoring parameters. A novel strategy for anomaly prediction of hard disk based on Mahalanobis distance using SMART attributes is also suggested in this paper. Furthermore, a case study of HDD anomaly prediction based on the methodology presented in this paper is carried out. The experiment results showed that the proposed method is feasible.

Research on features for diagnostics of filtered analog circuits based on LS-SVM

Feature selection techniques have become an apparent need for diagnostic methods such as a least squares support vector machine (LS-SVM). Most researchers use wavelet transform coefficients of the time-domain transient response data obtained from filtered analog circuits as features to train a LS-SVM classifier to diagnose faults. But wavelet coefficient features have certain disadvantages such as no physical meanings. Thus, in this paper, two new feature vectors with clearly defined meanings based on a time-domain response curve and a frequency response curve of a filter are proposed, respectively. In addition, a statistical property feature vector which represents global properties of the time-domain response curve or the frequency response curve is proposed. The results from the simulation data and real data for a biquad filter showed the following: (1) these proposed conventional time-domain and frequency features, which are already familiar to designers of filtered analog circuits, have good diagnostic accuracy—all above 91% for the example circuit; (2) the best accuracies using the proposed statistical property feature vector are 100% for time-domain simulation data, and for both real experiment data ; (3) the diagnostic accuracy using the proposed combined feature vector is more accurate than conventional feature vectors; (4) an LS-SVM can be used to diagnose faults in a real analog circuit that only has a few fault samples.

Rolling element bearing fault feature extraction using EMD-based independent component analysis

This paper introduces a joint bearing fault characteristic frequency detection method using empirical mode decomposition (EMD) and independent component analysis (ICA). Independent component analysis can be used to separate multiple sets of one-dimensional time series into independent time series, which need at least two transducers to obtain more than one set of time series for separation of different sources. To overcome this restriction, preprocessing is needed to construct multiple sets of time series. Empirical mode decomposition has attracted attention in recent years due to its ability to self-adaptively process non-stationary and non-linear signals with multiple intrinsic mode functions being obtained through EMD decomposition. Hence, considering this superiority, this paper employs EMD to transform one set of one-dimensional series into multiple sets of one-dimensional series for pre-processing. After that, independent components (IC) are extracted, which include fault-related signatures in the frequency spectrum. To validate the proposed method, real motor bearing vibration data, including normal bearing data, outer race fault data, and inner race fault data, are used in a case study. The results show that the proposed method can be used for bearing fault extraction.

Cooling fan bearing fault identification using vibration measurement

As a commonly used assembly in computer cooling systems, the normal operation of a cooling fan is critical for guaranteeing system stability and reducing damage to electronic components. Reliability analyses have shown that fan bearing failure is a major failure mode. Therefore, it is necessary to conduct research on fault detection of cooling fan bearings. In this paper we propose vibration-based fan bearing fault detection through the wavelet transform and the Hilbert transform. An experiment on fan bearings was conducted to collect vibration data for the validation of our proposed method. The analysis results show that the proposed method can identify different bearing faults.

Complex system maintainability verification with limited samples

Abstract Complex system maintainability verification is always a challenging problem due to limited sample sizes. Consequently, conducting maintenance experiments in a laboratory environment is an appropriate way to obtain data for maintainability verification. In maintenance experiments, faults are seeded in the equipment and maintenance activities are implemented to record repair time. In this process, two problems arise when laboratory experimental data (in-lab data) are used together with field data during the operational test and evaluation stage. The first problem is the verification of segmental maintenance data and the second one is the combination of in-lab data and field data for integrative maintainability verification. Regarding the problems mentioned above, this paper proposes a suitable methodology to solve them. Firstly, the idea of segmentally weighted verification is adopted and the segmentally weighted verification (SWV) method is proposed to realize in-lab data verification. Secondly, the Dempster–Shafer (D–S) evidence theory based integrative verification method is presented to solve the problem of in-lab and field data combination. A case study concerning radar system maintainability verification is presented as an example of the implementation of complex system maintainability verification in industry.