A study on vessel applicable indoor environment monitoring system

1. Introduction

Recently, with the increased interest in indoor fine dust, the interest in improving indoor environments to maintain a comfortable indoor space is also increasing [1].

In accordance with the recent amendments to the Indoor Air Quality Management Act, the maintenance and management of indoor air quality in apartment houses and public transportation vehicles is regulated. In public transportation (buses and trains), the research on indoor environment measurement and measurement systems is being actively conducted; however, the research on ships is insufficient [2].

Compared to general structures including land transportation, it is essential for ships to maintain the confidentiality of the outer shell for water immersion protection and crevice wind protection because of stability and space restrictions. Accordingly, the indoor environment of ships can be exposed to pollutants for a long time [3].

Therefore, in this study, a ship-applied indoor environment monitoring system is developed that combines an indoor environment measurement system and a Wi-Fi-based data communication system.

This paper is organized as follows: Section 2 introduces the current indoor environment management standards based on the Air Quality Control Act and related systems applied to land transportation. In Sections 3 and 4, the design and performance evaluation results of the indoor environment monitoring system for ships are presented. Finally, concluding remarks are presented in Section 5.

2. Indoor Environment Management Standards and Related Systems

In the Indoor Air Quality Management Act for public facilities, indoor space pollutants are divided into five maintenance requirements and five recommended standards, and these pollutants are managed according to the relevant laws of the relevant ministries. Table 1 shows the indoor air quality management status defined by the Ministry of Environment and Ministry of Health and Welfare [5].

Table 1:

Indoor air quality management status by ministry

Efforts are being made to maintain a comfortable indoor environment based on these management regulations, and various studies are being conducted on indoor environment monitoring for this purpose [3].

As an indoor environment monitoring technology for land transportation, the London underground and Siemens mobility provide indoor environment information through an in-train display after applying air monitoring and ride comfort monitoring to new railway vehicles currently operating in the London underground system [6]. Hyundai Rotem is developing a service that measures the fine dust, ultrafine dust, temperature, and humidity in a train and provides passengers with cabin congestion and fine dust figures [7].

3. System Design

3.1 System implementation

In this study, an IoT-based indoor environment monitoring system is designed. The system consists of a monitoring sensor module that collects real-time indoor environment information, a calculation and data processing module that analyzes information and transmits control signals, and a Wi-Fi-based communication module. Each sensor collects information about the indoor environment, integrates the collected information within the sensor module, and delivers it to the indoor environment integrated management module server through a wireless network.

Figure 1 shows the system architecture showing the overall operational flow of the system to be developed and the configuration of each module.

Figure 1:

System architecture

The measurement equipment for monitoring the indoor environment of a ship and maintaining and improving the indoor environment at an appropriate level must acquire data on indoor environment information (temperature, humidity, and fine dust) and indoor activity information (vibration and noise). The information collected from each sensor module is presented in Table 2.

Table 2:

The collected information for each sensor

3.2 Outlier removal algorithm

The actual indoor environment changes continuously at a slow rate. However, in the case of an indoor environment monitoring system in a ship, it is an environment in which the outliers are prone to occur because of challenges such as communication errors due to closed spatial characteristics.

Figure 2 shows the actual collected PM 2.5 (particulate matter less than 2.5) data. In this figure, the x-axis is time in minutes, and the y-axis is the concentration of PM 2.5. The overall signal is stable; however, a signal with an exceptionally high transient value can be observed, as represented by the red box. The majority of these are communication errors that occur in the shielding environment of ships, which makes it difficult to monitor and manage the indoor environment through this system.

Figure 2:

Collected signals and noises

To address this problem, an outlier removal algorithm is proposed, as shown in Figure 3. The mean and standard deviation were calculated by statistically analyzing the data of the previous 24 h based on the measurement time, and the calculated statistical values were converted into a Z-score. Based on this result, it is determined whether the data at the current time point is normal or abnormal, and if it is abnormal, the outlier is replaced by a moving average filter.

Figure 3:

Proposed algorithm

Using Equation (1), the Z-score value Z_t can be derived.

$\[ Z_t=\frac{X_t-{\mu }_{\left(24h\right)}}{{\sigma }_{\left(24h\right)}} \]$

(1)

where, X_t is the air condition at time t, μ_(24h) and σ_(24h) are the mean and standard deviation, respectively. Z_t becomes a criterion for filtering, as shown in Equation (2).

(2)

The estimation value of Equation (2) is determined through the threshold, and X_t is the moving average.

4. Experiment and Performance Verification

4.1 Proposed algorithm feasibility analysis

To verify the validity of the proposed outlier removal algorithm, it was compared and analyzed with widely used IQR and DBSCAN [8, 9]. The experiment was based on the data collected for a single day by installing the actual equipment on the ship. As shown in Figure 4, the Z-score applied in this study displayed the best accuracy.

Figure 4:

Comparison of accuracy by method

4.2 Performance verification

To evaluate the actual performance of the proposed indoor environment monitoring system, it was installed in an actual vessel environment, and experiments were conducted.

Figure 5 and 6 show the raw measured data of PM 2.5 and the same filtered result, respectively. It can be seen that the outliers generated in Figure 5 are all removed as a result of the filter in Figure 6.

Figure 5:

The measurement data of PM 2.5 (raw data)

Figure 6:

The measurement data of PM 2.5 (filtered data)

5. Conclusion

In this study, an indoor environment monitoring system and an outlier removal algorithm to minimize noise for the efficient management of indoor air quality in ships was proposed. To verify the performance of the proposed system, it was installed on an actual ship, and the experimental data were collected. The experimental results confirmed that the proposed system can be operated in a ship environment.

Acknowledgments

This research is the result of a study on the “Leaders in Industry-university Cooperation+” Project, supported by the Ministry of Education and National Research Foundation of Korea.

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