Recent findings suggest that abnormal operating conditions of equipment in the oil and gas supply chain represent a large fraction of anthropogenic methane emissions. Thus, effective mitigation of emissions necessitates rapid identification and repair of sources caused by faulty equipment. In addition to advances in sensing technology that allow for more frequent surveillance, prompt and cost-effective identification of sources requires computational frameworks that provide automatic fault detection. Here, we present a changepoint detection algorithm based on a recursive Bayesian scheme that allows for simultaneous emission rate estimation and fault detection. The proposed algorithm is tested on a series of near-field controlled release mobile experiments, with promising results demonstrating successful detection (>90% success rate) of changes in the leak rate when the emission rate is tripled after an abrupt change. Moreover, we show that the statistics of the measurements, such as the coefficient of variation and range are good predictors of the performance of the algorithm. Finally, we describe how this methodology can be easily adapted to suit time-averaged concentration data measured by stationary sensors, thus showcasing its flexibility.

翻译：最近的调查结果表明,石油和天然气供应链中设备的异常运行条件是人为甲烷排放量的很大一部分。因此,要有效减少排放,就必须迅速查明和修理故障设备造成的排放源。除了能够更频繁地监测、迅速和以成本效益高的方式查明来源的遥感技术的进步之外,还需要有能够自动发现故障的计算框架。在这里,我们根据一个可同时估计排放率和发现故障的循环巴伊西亚计划提出改变点检测算法。提议的算法是用一系列近地控制释放移动实验测试的,有希望的结果显示,在排放率在突然变化后达到三倍的情况下,成功检测出漏漏率的变化(>90%的成功率)。此外,我们还表明,测量数据,例如变异系数和范围是算法性表现的良好预测因素。最后,我们说明,该方法如何可以很容易地适应由固定传感器测量的按时平均浓度数据,从而显示其灵活性。