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It is understood that an estimate of uncertainty on the output voltage and the duty provides an estimate of uncertainty on the transfer function response. The theoretical validation of uncertainty on the measured transfer function is

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evaluated by modelling flicker noise source along with the quantisation noise source of the quantisers.

A second-order closed-loop buck converter, 3.3 V input and 1.2 V output, with power-train component values equivalent to 𝐿 = 150 µH, 𝐶 = 220 µF, 𝑟_𝐶 = 90 mΩ, 𝑟_𝐿+𝑆 = 840 mΩ and 𝑅_{𝐿𝑜𝑎𝑑} = 10 Ω is modelled in SIMetrix. Additionally, the quantisation noise variance of the ADC and DPWM is modelled to understand their influence on the output voltage and the duty.

6.3.2.1 Quantisation Noise Model

An ideal N-bit quantiser causes a maximum error of ±1

2𝐿𝑆𝐵 when digitising an analog signal where 1 LSB or quantisation level ∆ is defined as

∆= 𝑉𝑚𝑎𝑥− 𝑉𝑚 𝑛

2𝑁−1 (6-10)

where 𝑉_𝑚𝑎𝑥− 𝑉_{𝑚 𝑛} represents full-scale amplitude of the analog signal.

The non-linear quantisation noise due to rounding has a zero mean with average noise power

𝜇_𝑄𝑅 = 0 ; 𝜎_𝑄𝑅2 = ∆ 2

12 (6-11)

Consequently, the truncation error has a non-zero mean with average noise power described by

𝜇_𝑄𝑇 = ∆

2 ; 𝜎𝑄𝑇 2 ₌ ∆2

3 (6-12)

Assuming the total quantisation noise is only due to rounding, the quantisation noise can be modelled as an additive, signal-independent, white noise uniformly distributed over the interval [−∆

2, + ∆

2] with variance defined by 𝜎_𝑇2 = ∆

2

12 (6-13)

where 𝜎_𝑇2 is the variance of the quantisation noise. Subsequently, the flat and uniformly-distributed power spectral density of quantisation noise measured over

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the full Nyquist bandwidth from DC up to half the switching frequency 𝐹𝑆

2 can be defined as 𝑆𝐷_𝑇 = 𝜎𝑇 2 (𝐹𝑠 2 ⁄ ) = ∆2 6 𝐹_𝑠 (6-14)

From the above known quantisation noise characteristics, the quantisation noise of the ADC and DPWM are modelled in SIMetrix. The quantisation noise is generally modelled as a white noise source. A white noise is random in nature and exhibits uniform distribution over the frequency of interest. However, some researchers disagree on the whiteness of the quantisation noise. However, in most cases, white noise source can be effectively modelled as a quantisation noise source.

Moreover, when the voltage resolution of the quantisers is small, compared to the magnitude of the signal, it is reasonable to assume that the quantisation error is stationary white noise and uniformly distributed over the interval ±∆

2.

The simplest method to implement a white noise source is to use a resistive noise model. The noise of an ideal resistor commonly known as thermal noise or Johnson-Nyquist noise is flat with a power spectral density 𝑆𝐷𝑇𝐻 per hertz of bandwidth expressed as

𝑆𝐷_𝑇𝐻 = 4 𝑘_𝐵𝑇 𝑅 _(6-15)

where 𝑘_𝐵 defines the Boltzmann constant in Joules/Kelvin, 𝑇 is the operating temperature measured in Kelvin, and 𝑅 is the resistance expressed in ohms. Using (6-14) and (6-15), an equivalent resistance that exhibits quantisation noise can be calculated

𝑅 = ∆

2

24 𝑘_𝐵 𝑇 𝐹_𝑠 (6-16)

Now, by simulating noise analysis over the Nyquist bandwidth from DC to half the Nyquist frequency with the above noise sources, the noise spectral density on

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output voltage and duty can be estimated. The effect of quantisation noise of the ADC and DPWM is estimated individually on the output voltage and the duty. Firstly, the noise spectral density on output voltage is predicted by assuming that the noise in the system is only due to the quantisation noise of the ADC, i.e.

σ𝑣_𝑜(𝐴𝐷𝐶) = σ𝐴𝐷𝐶 ; σ𝐷𝑃𝑊𝑀~0 (6-17) Subsequently, the influence of DPWM quantisation noise is predicted by assuming there is no quantisation noise introduced by the ADC, i.e.

σ_{𝑣𝑜(𝐷𝑃𝑊𝑀)} = σ_{𝐷𝑃𝑊𝑀} ; σ_𝐴𝐷𝐶~0 (6-18) Hence, the total noise spectral density on the output voltage is computed as the square root of the sum of squares of the quantisation noise of the ADC and the DPWM measured individually at the output of the ADC.

σ_𝑣𝑜 = √σ_𝑣2_𝑜(ADC)+ σ_𝑣2_𝑜(DPWM) (6-19) Similarly, the noise spectral density on duty (6-22), square root of the sum of squares of the quantisation noise of the ADC and the DPWM, is measured individually at the output of the DPWM using (6-20) and (6-21)

σ_{𝑑(𝐴𝐷𝐶)} = σ_𝐴𝐷𝐶 ; σ_{𝐷𝑃𝑊𝑀}~0 (6-20)

σ_{𝑑(𝐷𝑃𝑊𝑀)}= σ_{𝐷𝑃𝑊𝑀} ; σ_𝐴𝐷𝐶~0 (6-21)

σ_𝑑 = √σ_d(ADC)2 _{+ σ}

d(DPWM)

2 _(6-22)

The simulated results illustrated in Figure 6-6 a) and b) reveal that quantisation noise of the ADC largely influences the noise spectral density on the output voltage and the duty, compared to the quantisation noise of the DPWM. Note that orange curve is below the grey curve. However, the above model does not include the characteristics of flicker noise.

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Figure 6-6 Noise Spectral Density due to ADC and DPWM Quantisation Noise a) Output voltage b) duty

6.3.2.2 Flicker Noise Model

The flicker noise, also known as 1/𝑓 noise, is an electronic noise governed by low-frequency noise and a high-frequency flat band white noise. It is often characterised by a corner frequency 𝑓_𝑐. The corner frequency governs the power spectral density of the flicker noise. In particular, the power spectral density is inversely proportional to the frequency of the signal below the corner frequency and a flat power spectrum above the corner frequency.

The measured noise spectral density in Error! Reference source not found. demonstrates flicker noise with a corner frequency of approximately 3 kHz and a flat white noise spectral density equivalent to three times the variance of the ADC quantisation. Based on the above characteristics of the flicker noise, a resistive flicker noise source is modelled to understand the residual inconsistency in the SNR on the transfer function.

The resistive flicker noise parameters in the SIMetrix Simulator Reference Manual define the power spectral density of the flicker noise as

PSD𝐹𝐿 ∝ KF

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where EF defines the flicker noise exponent and KF is the flicker noise coefficient. The modelled noise spectral density including the above noise characteristics demonstrates that majority of the uncertainty on the measured output voltage is due to resistive flicker noise source as illustrated in Figure 6-7 a).

Figure 6-7 Noise Spectral Density on a) Output Voltage b) Duty

In contrast, the flicker noise modulates the same effect on the duty as shown in Figure 6-7 b). The theoretical quantification of noise spectral density on output voltage and duty due to quantisation effect of quantisers and the flicker noise justify the presence of high relative standard deviation on the transfer function. Conversely, it is evident that flicker noise significantly increases the level of uncertainty on the output voltage and hence on the transfer function.

This uncertainty significantly affects the overall performance of the health- monitoring methodology. It can be summarised that based on the careful selection of perturbation amplitude, the uncertainty on the measurements is influenced by different noise sources in the system except the digital quantisation noise.

Therefore, the above analysis concludes that it is imperative to understand and acknowledge system-inherent noise sources prior to measurements. This is

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because the presence of such noise sources significantly degrades the SNR of the measurements which consecutively propagate uncertainty on the measurements, the system identification algorithm and finally, on the estimation of power-train component values.