The failure of the measurement tube is a key factor affecting and constraining the measurement accuracy, reliability, and safety of Coriolis flow meters. Wall attachment failure is one of the forms of measurement tube failure that can easily occur during the service life of Coriolis flow meters. The occurrence of wall attachment failure changes the physical rigidity of the Coriolis flow meter, leading to a shift in the calibration factor, which directly affects the measurement accuracy of mass flow and other fluid information. Furthermore, if wall attachment failure is not promptly warned, its progression may result in pipeline blockage and, in severe cases, even explosions, posing significant industrial safety risks. Therefore, detecting the service status of Coriolis flowmeters and identifying wall attachment faults in the measuring tube are urgent needs to improve the measurement accuracy, reliability, and safety of Coriolis flowmeters. To address this issue, this paper proposes a wall attachment fault detection method based on a combination of band-stop filters and sample entropy. Since vibration response signals typically contain multiple modal characteristic signals as well as interference signals, the combined band-stop filter effectively eliminates interference while preserving target modal characteristic signals. By calculating the sample entropy of modal characteristic signals under different states, the method fully utilizes its high sensitivity to dynamic signal changes. When a fault occurs, the complexity of the signal increases significantly, causing the sample entropy value to change accordingly, providing a reliable basis for fault diagnosis and evaluation. By comparing the sample entropy values of normal and faulty states, it is possible to quantitatively analyze the severity of faults, thereby achieving effective monitoring of the flow meter′s fault conditions. The experimental results indicate that this method can effectively identify the wall-hanging faults of the Coriolis flowmeter measurement tube, outperforming existing methods in fault detection accuracy and reliability.