LIFETIME PREDICTION OF SINGLE-STAGE LED DRIVER CIRCUIT USING BAYESIAN BELIEF NETWORK: Lifetime prediction of LED driver using Bayesian Belief Network (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2023)
In light-emitting diode (LED) lighting, the driver commands the lifetime and the LED bulb. It is essential to predict the driver's lifetime during its design stage. This paper proposes a viable method to predict the lifetime of the LED driver based on its failure rate. A novel single-ended primary inductor converter (SEPIC) integrated parallel ripple cancellation circuit (PRC) topology of 30 W is considered to estimate the lifetime of the driver circuit. The failure rate model is regarded as a base to predict the lifetime of LED drivers using Bayesian belief network (BBN) analysis. The weakest links are the prime components to estimate the lifetime, and they are active devices and capacitors employed in the driver circuit. The output capacitor is considered a degree of importance as per the existing literature, and the proposed course has been designed without using any electrolytic capacitor (EC) to enhance the reliability of the driver circuit. This approach ensures an effective way to access the mean time to failure (MTTF) or the lifetime of the LED driver in a lucrative manner.
Keywords: Light-emitting diode (LED); Single-ended primary inductor converter; Parallel ripple cancellation circuit; Bayesian belief network (BBN); Electrolytic capacitor (EC); Mean time to failure (MTF)