Preventive maintenance aims to keep components in good operating condition and to reduce the probability of failure. Optimizing the frequency of preventive maintenance continues to be an important research topic because it can significantly impact operating costs in industrial settings. A key factor in optimizing preventive maintenance is the calculation of the probability of component failure. Estimates of this probability have commonly been based on historical data and the maintenance quality. The improvement factor (IF) was introduced in the literature as a way of measuring maintenance quality so managers and planners could better estimate the probability of component failure, post maintenance. The IF estimate can be based on either historical failure data or expert judgment. It has been demonstrated in previous works that relying on expert judgment is the better of the two approaches when estimating the IF. Such judgment is, however, inherently biased. We propose a framework for estimating the IF based on expert judgment that aims to circumvent such biases. To demonstrate the effectiveness of the proposed framework, we estimate the optimum maintenance interval of a system at a real-world offshore oil and gas installation. The results show our framework yielding an improved maintenance interval.