Of Eagle's Syndrome and Finding Some Clarity on Its Management.

Eagle's Syndrome is a much discussed yet controversial and debatable diagnosis of exclusion which is treated by many specialities with often unsatisfactory results. Due to entrapment/impingement on surrounding neurovascular structures by elongated styloid process patient may present with multitude of symptoms. Treatment is controversial and opinions are divided on choice of conservative and surgical management. Aim was to study outcomes of conservative and surgical modalities of treatment of Eagle's Syndrome and bring some clarity on management, what to offer, to whom and when. This prospective observational descriptive study included 15 patients of Eagle's Syndrome, 7 were treated with conservative method and 8 underwent resection of styloid process with intraoral approach. With objectives in mind to study efficacy of both management modalities, pain visual analogue scale (VAS) scores were recorded pre-intervention, post-intervention and during follow up on 1, 3 and 6 months and compared. Conservative management resulted in up to 70% reduction in pain VAS scores till 3 months of therapy (mean pre-intervention score being 3.71, 3 months-1, 6 months-1.29), while surgical modality resulted in nearly 99% reduction in mean pain VAS scores up to 3 months and even improved after 6 months (mean pre-intervention score being 6.75, 3 months-0.5, 6 months-0.13). With this we can conclude that conservative management provide satisfactory short-term (up to 3 months) results but recurrences are known, while surgical resection of elongated styloid process gives better long-term results (6 months and beyond).

Selection of efficient degradation features for rolling element bearing prognosis using Gaussian Process Regression method.

Rolling Element Bearings are one of the most ubiquitous machine elements used in various machineries in the manufacturing industry. Prognosis and estimation of residual life of rolling element bearing are very important for efficient implementation of health monitoring and condition-based maintenance. In this paper, a rolling element bearing fault or degradation trend prediction is modeled using Gaussian Process Regression (GPR) method. Various vibration features based on signal complexity, namely Shannon entropy, permutation entropy, and approximate entropy are estimated to obtain the bearing degradation trend. When fault or degradation occurs in rolling element bearing, there is a subtle change in the dynamics of the system and subsequently, there are changes in the features extracted from the vibration signal. In this paper, a comparative analysis of various kernel functions of the GPR model is carried out using accuracy-based metrics. In addition, the combination of goodness of metric (monotonicity (Mon), robustness (Rob), and prognosability (Pro)), namely hybrid metric, is proposed to select the efficient bearing degradation trend of features. Further, the GPR at ARD exponential kernel has been employed to make the prognosis of degradation trend in bearings with a 95% confidence interval (CI). The proposed methodology is validated through a mathematical model of the simulated vibration signal. Finally, from the simulated and experimental data, it is demonstrated that the entropy features have better performance than the statistical features.