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Ensuring the accuracy of flow measurement is crucial to promoting high-quality cigarette production. In order to monitor the working status of flowmeters, this paper proposes an anomaly detection method based on the sliding-window recursive Lasso (Least absolute shrinkage and selection operator), which is able to track the changes in flowmeter operating conditions by self-adapting model parameters based on observed measurements. Due to the frequent mode switch and high sampling frequency of flow data, this paper introduces the sliding-window strategy to remove the effect of outdated data and accelerate the optimization. The tracking errors are used as a measure of anomaly and different thresholds are introduced based on the operating manual of cigarette production, which are used to distinguish between mode switch and flowmeter anomalies. The method’s effectiveness is verified by detecting flowmeter anomalies in a real cigarette production line. The mean absolute error (MAE) is 8.1479 and the root mean squared error (RMSE) is 2.8544, which outperforms methods such as Lasso and the ridge regression.

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