Abstract
Prognostic of electronic device under vibration condition can help to get information to assist in condition-based maintenance and reduce life-cycle cost. A prognostic and remaining life prediction method for electronic devices under random vibration condition is proposed. Vibration response is measured and monitored with acceleration sensor and OMA parameters, including vibration resonance frequency, especially first-order resonance frequency, and damping ratio is calculated with cross-power spectrum density (CPSD) method and modal parameter identification (MPI) algorithm. Steinberg vibration fatigue model which considers transmissibility factor is used to predict the remaining life of electronic component. Case study with a test board is carried out and remaining life is predicted. Results show that with this method the vibration response characteristic can be monitored and predicted.
Highlights
Prognostics and Health Monitoring (PHM) of electronic devices integrates sensor data with models that enable assessment of the degradation of a product from an expected normal operating condition and assesses the future reliability of the product based on current and historic conditions [1]
PHM of electronic devices under vibration condition has been studied by some researchers, which is mainly based on two kinds of method, data-based method and model-based method
Lall et al developed a statistical method for prognostication of area-array electronics under shock and vibration loads during vibration testing, which is based on state space vectors from resistance spectroscopy measurements [2]
Summary
Prognostics and Health Monitoring (PHM) of electronic devices integrates sensor data with models that enable assessment of the degradation of a product from an expected normal operating condition and assesses the future reliability of the product based on current and historic conditions [1]. For model-based prognosis approach, modal parameters, such as frequency and damping, are associated with the prediction of remaining useful life, which will change during the process of work Recent research evaluates these parameters by simulation with FEA or CalcePWA software, which may add offline procedures and may need more experience of skilled person. Chesneand Deraemaeker studied the application of Steinberg model in tantalum capacitor [18] Through experiment, they provided the S-N curve in different sinusoidal sweeping-frequency vibration conditions and compared the experimental result with Finite Element Analysis (FEA) simulation, with the aim of determining parameter values of Steinberg model. Vibration response is measured and monitored with acceleration sensor under white noise random excitation, and OMA parameters, including vibration resonance frequency, especially firstorder resonance frequency, and damping ratio, are calculated with cross-power spectrum density (CPSD) method and modal parameter identification (MPI) algorithm.
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