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              <p>The electronic boards in drilling and measurement (D&amp;M) tools pro-vide multiple functions, such as data acquisition, signal processing, op-eration control, and data storage. However, due to the harsh downholeoperating conditions; i.e., high temperature, dynamic vibration, and ex-tensive shocks, the boards are likely to suffer from complex failure modesand result in failed jobs. Estimating the risk level of the boards cantolerate and provide support for maintenance decision making and jobplanning, this paper presents a statistical method for risk assessment ofthe electronic boards. The method first selects relevant channels fromD&amp;M tool measurement data and extracts histogram features based onthose selected channels. The histogram features are then enhanced basedon a linear interpolation method and aggregated using weighted sum. Fi-nally, hidden Markov models (HMMs) with different parameter settingsare trained using the processed features. The best HMM is chosen accord-ing to the Akaike information criterion and Bayesian information criterion.The proposed HMM-based method is tested on a real-world data set offailed control processing unit boards that were assembled for a specificD&amp;M tool. The experimental results show that this method is effectivein estimating the risks as a sequence of events, which in turn, helps toachieve consistent risk estimation. The work presented in this paper isalso part of a long-term project with the aim to construct a risk-baseddecision advisor for D&amp;M tools used in the oil and gas industry.</p>
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