## 1 Introduction
However, this validation is not perfect; for example, the signal-to-noise ratio of the signal is not fully considered, and the signal is not obtained based on quasi-periodic sampling. For the above problems, this paper adopts the 95% confidence interval algorithm to calculate the peak-to-peak value of the signal in order to alleviate the suppression of the high-frequency components of the signal by noise. At the same time, in order to avoid the problem of using three-axis acceleration data for posture solving, which cannot be used to obtain the heading angle, and the problem that the use of three-axis gyroscope data for calculating the posture of the car alone requires the time integration of the angular velocity, a six-axis acceleration gyroscope sensor is used to calculate the posture of the cabin. The results obtained from the two calculations are complementarily fused, thus further improving the monitoring of elevators by the designed system. In addition, in the design of an elevator fault operation monitoring system, [PERSON] et al. [12] designed a microprocessor-based monitoring system that uses multiple independent sensors to collect an elevator's operation status in real-time; [PERSON] et al. [13] proposed the design of an elevator remote monitoring system based on a customized communication protocol; [PERSON] et al. [14] extracted characteristic parameters for elevator car vibration data and diagnosed elevator faults through big data analysis; [PERSON] et al. [15] designed a non-intrusive elevator operation state monitoring method to complete the tracking of real-time elevator positions by using the traceless Kalman filter algorithm, which integrates the synchronous positioning and map building techniques; [PERSON] et al. [16] used a wireless radio frequency system that is able to obtain and save an elevator's operation data in real-time; [PERSON] [17] designed a WiFi three-axis wireless vibration sensor that is able to directly transmit an elevator's vibration signals to the cloud platform to achieve remote data collection; [PERSON] et al. [18; 19; 20] analyzed the vibration signals of an elevator through the collection of data after the use of a variety of algorithms to monitor the elevator's fault information; [PERSON] et al. [21] used a GPS system and pressure sensor to calibrate the height signal; and [PERSON] et la. [22] used temperature and humidity to correct the pressure sensor. Although the above monitoring techniques of an elevator's operating status have achieved certain results, the barometric pressure sensor applied in height monitoring is susceptible to data fluctuations due to environmental influences, and the barometric pressure sensor may produce zero drift in an environment with stable air pressure; if the use of other height monitoring techniques (GPS, laser, and radar) is too expensive, they are rarely considered. This paper is based on BP neural network training to eliminate the impact of the zero drift of a barometric pressure sensor on the height error so as to make an elevator's height monitoring more accurate and effective. In summary, fault diagnosis systems for traction elevators have been widely used and have achieved remarkable results. However, previous studies are based on using sensors to collect data and using cloud servers for fault diagnosis. In this paper, a non-intrusive, online, height-integrated monitoring system for traction elevators, based on NB-IoT (Narrow Band Internet of Things) and STM32, is designed. The 95% confidence interval algorithm is used to calculate the peak-to-peak value of the signal and improve the signal-to-noise ratio of the vibration signal, while a six-axis acceleration gyro sensor is used to realize the complementary fusion of the posture solution; temperature compensation and BP neural network model are used to eliminate the abnormal fluctuation of the data when the barometric pressure sensor monitors the height. The system does not change the existing structure of an elevator and does not use the elevator manufacturer's interface.