The integration level and energy consumption of electronic circuits is increasing, resulting in increased heat flux densities and temperatures in electronic devices. In addition, the recent trend toward super-light and super-thin electronics has imposed further challenges on system thermal design. According to [1
], temperature has a great impact on electronic component reliability, and the failure rate of a component increases exponentially as the temperature increases. Therefore, it is necessary to utilize thermal design techniques so as to reduce the internal temperature of electronic devices. The working principle of thermal design is to accomplish the following: (1) lessen heat dissipation by utilizing low energy consumption techniques and reducing the number of heat-generating components; and (2) move heat out through conduction, convection, or radiation. Cooling fans, as an active heat transfer device, have been used in many electronics systems to lower the system temperature and improve reliability.
As a commonly used thermal solution for most electronic devices, cooling fans have simple structures with low cost. According to [2
], cooling fan failure is a major problem for many electronic devices. It causes system instability, malfunctioning, and damage to electronic components by over-heating, and can finally lead to system failure [3
]. This may result in severe economic, or even catastrophic, losses under certain applications, such as large-scale data servers in financial divisions, communication networks, avionics, medical devices, etc.
Therefore, it is necessary to conduct research on cooling fan condition monitoring and health assessment to guarantee the normal operation of a fan.
A cooling fan is composed of both electronic and mechanical parts. The mechanical parts include the bearings, shaft, fan blades, and fan housing; out of these, bearing failure is the top contributor to fan failure. The types of bearings used in cooling fans can be categorized as sleeve bearings, ball bearings, fluid bearings, and magnetic bearings. The selection of bearings should consider parameters such as performance, durability, cost, size, weight, and noise. Ball bearings have the advantage of a good balance between these factors, and so they are widely used in cooling fans. Specifically, ball bearings have a longer lifespan at higher temperatures (63,000 hours at 50 °C) than sleeve bearings (40,000 hours at 50 °C) [3
As a typical rolling-element bearing, a ball bearing is the fundamental rotating part in a mechanical system, and numerous studies have been conducted on bearing fault diagnosis [4
]. Regarding the current progress on machinery health assessment, Miao et al.
developed gear health assessment methods using empirical mode decomposition [13
] and wavelet decomposition [14
]. Wang et al.
] presented gearbox fault diagnosis and prognosis by the fusion of multiple health indicators through support vector data description. Yang and Makis [16
] used an ARX model to evaluate gearbox health conditions under variable load conditions. Lin et al.
] proposed an approach for gearbox condition-based maintenance, and the fault growth parameter was defined using the residual error signal. Qiu et al.
] proposed a self-organizing-map-based performance degradation method for assessing bearing health condition. Ocak et al.
] developed a new scheme based on wavelet packet decomposition and the hidden Markov model for bearing prognostics. Pan et al.
] used wavelet packet node energies as bearing fault features. Then, fuzzy c-means [20
] and support vector data description [21
] were respectively employed to evaluate how far the current bearing health condition was from normal bearing health condition. In their following up studies, Pan et al.
] proposed a hybrid model for bearing performance degradation utilizing support vector data description and fuzzy c-means. Jiang et al.
] proposed a new approach combining the autoregressive model and fuzzy cluster analysis for bearing diagnosis and degradation assessment. Shen et al.
] considered the cumulative characteristics of bearing performance deterioration and proposed a monotonic health index for evaluating bearing health condition. Lei et al.
] proposed health indicators for monitoring planetary gearboxes health condition.
The purpose of this research is to investigate cooling fan bearing health assessment methods and develop a prognostics and health management (PHM) solution for fan degradation assessment. However, the literature on fan bearing health assessment is limited. The Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland, has conducted research on fan bearings, including fan bearing fault identification [3
], a physics-of-failure approach for fan PHM [26
], a precursor monitoring approach for cooling fans [27
], and fan bearing degradation using acoustic emission [28
]. This paper proposes a new health indicator for fan bearing degradation assessment. The comblet, which was initially proposed by Miller [29
] for gearbox vibration analysis, is utilized for the extraction of fault-sensitive information from the frequency domain of the bearing vibration signal. The health indicator is defined by data taken from the bearing vibration spectrum, incorporating the idea of exponentially weighted moving average (EWMA). The proposed EWMA based health indicator can utilize historical information (current and previous data) about the test sample, and it does not require model training, as opposed to other related studies [18
]. To validate the proposed method, a test rig for a cooling fan accelerated life test was established, and a set of fan bearing vibration data collected from the test rig was used.
The rest of this paper is organized as follows: Section 2 introduces the fundamentals of the comblet filter. In Section 3, a new health indicator for fan bearing degradation assessment is proposed. In Section 4, the fan bearing accelerated life test rig is introduced, and then vibration data collected from this test rig are used for validation of the proposed method. Conclusions are presented in Section 5.
Cooling fans are commonly used in microelectronics. In order to ensure high reliability in air-cooled electronic systems, it is necessary to conduct research on the life expectancy and health assessment of cooling fans. Fan bearing failure is a major failure mode that causes excessive vibration, noise, reduction in rotation speed, locked rotor, and failure to start, among other problems, which may result in an electronic system's malfunction and lower the electronics reliability.
This paper presents a coherent solution for the health assessment of cooling fan bearings. The method utilizes the comblet concept. A health indicator was proposed based on the techniques of comblet filtering and exponentially weighted moving average. An accelerated life test was conducted on a cooling fan to simulate fan bearing degradation. The recorded vibration data were used to validate the proposed method. To demonstrate the performance of the proposed method, a comparative study was conducted between the proposed HCI and the commonly used methods of RMS, kurtosis, and FGP1. Based on the analysis results, the HCI can detect incipient fan bearing failures, and the bearing degradation process can be captured by the proposed method.
The work presented in this paper provides a promising method for cooling fan bearing health evaluation and prognosis. With this method, the critical failure of a cooling system can be avoided, and the reliability of electronic systems can be guaranteed. Furthermore, the proposed solution may also be used in generic bearing health evaluation and prognosis, which is currently the focus of prognostics and health management of mechanical systems.