空氣品質感測器之故障偵測--基於深度時空圖模型的異常偵測框架

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姓名

林子恆(Tzu-Heng Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

空氣品質感測器之故障偵測--基於深度時空圖模型的異常偵測框架
(Detecting Malfunctioned Air Quality Sensors -- an Anomaly Detecting Framework Base on Deep Spatial-Temporal Graph Model)

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摘要(中)

近年來空氣汙染已成為重視的議題。台灣行政院環保署規劃，於2017年至2020年逐步布建1萬200個空氣品質感測器，由物聯網方式蒐集大量且時間解析度密集的空品數據來分析各地區的空氣污染現象。

由於空氣品質感測器本體小且脆弱，容易受到日曬、雨淋或是其他物理因素使得裝置故障而偵測到錯誤的pm2.5數
值，而目前的空氣品質感測器故障偵測方式仰賴現場做檢閱測量。但感測器佈設點過多，以抽檢方式做巡檢是沒有效率的巡檢方式。

我們建構一個基於深度時空圖模型的異常偵測框架，模型包含圖卷積和時間卷積兩個方法，時間卷積處理過往空氣品質感測器偵測到的pm2.5時序資料，找出時間上的相關性，同時於每個時間點，對感測器與其周圍感測器點建立成一個無向聯通圖，由圖卷積的方式，找出空間特徵，最後模型預測當下的pm2.5數值，並與空氣品質感測器偵測到的數值計算R2-score分數，並對所有空氣品質感測器站點的R2-score做低到高排序，R2-score越低代表感測器越有可能出現異常。經過實驗，在有限巡檢個數之下，我們模型找到有問題個數的比率跟過往異常偵測的方法和隨機挑選都來的高，優先檢查本模型認定的故障感測器可降低巡檢所需的人力與時間成本。

摘要(英)

Air pollution is an essential issue in Taiwan. From 2017 to 2020, the Environmental Protection Administration of Executive Yuan in Taiwan has gradually deployed 10,200 air quality sensors, which have collected a large amount of air quality data with high time resolution. However, because the air quality sensors are small and fragile, environmental factors, such as sun, rain, and other physical characteristics, may influence or even damage these sensors. As a result, the monitored PM2.5 values from these malfunctioning sensors are inaccurate. The current strategy to identify the failure sensors requires on-site inspection. However, since the number of sensors is huge and sensors are located all over Taiwan, it is inefficient to discover the malfunctioning sensors by scheduled inspection or random sampling.

We propose an anomaly detection framework to identify the malfunctioning sensors based on a deep spatial-temporal graph model consisting of graph convolution and time convolution.

The time convolution discovers the temporal relationship among the monitored PM2.5 values of a sensor. At each time point, an undirected connected graph is established between each sensor and its surrounding sensors. The graph convolution utilizes these graphs to learn the spatial characteristics. We leverage this deep spatial-temporal graph model to predict the current PM2.5 value of a target sensor. We calculate the R2-score between the predicted PM2.5 values and the monitored PM2.5 values for each sensor and rank these sensors by their corresponding R2-scores in ascending order. We claim a sensor has malfunctioned if the R2-score is low (i.e., the predicted and the monitored PM2.5 values are very different). Experimental results show that our model identifies more problematic sensors with fewer trials. Consequently, examining the sensors with the order outputted by our model can save labor and time costs.

關鍵字(中)

★ 圖卷積網路
★ 時間卷積網路
★ 時序異常偵測
★ 深度學習

關鍵字(英)

★ Graph Convolution Network
★ Temporal Convolution Network
★ Time Series Anomaly Detection
★ Deep Learning

論文目次

摘要 iv
Abstract v
目錄 vii
圖目錄 x
表目錄 xii
一、緒論 1
1.1 研究動機 1
1.2 研究貢獻 3
1.3 論文架構 3
二、相關研究 5
2.1 統計與規則基礎方法 5
2.2 機器學習方法運用在空氣品質預測 5
2.3 深度時空圖模型方法介紹 6
三、研究方法與流程 8
3.1 產生圖序列資料 9
3.1.1 設定圖形結構中的邊權重 9
3.2 深度時空圖模型 10
3.2.1 圖卷積 Graph Convolution 10
3.2.2 時間卷積 Temporal Convolution 16
3.2.3 模型架構 17
3.2.4 STGCN 模型介紹 19
3.3 異常偵測模組 20
四、實驗結果 22
4.1 實驗流程與細節 22
4.1.1 空汙感測器資料集介紹 22
4.1.2 資料清洗 23
4.1.3 切割訓練資料與測試資料 23
4.1.4 選取周圍感測器個數 23
4.1.5 時間滾動產生訓練特徵與標籤 24
4.1.6 產生圖形時序資料 25
4.1.7 模型訓練 25
4.1.8 異常偵測模組 30
4.2 實驗結果 30
4.2.1 評量指標 30
4.2.2 ROC curve 分數結果 31
4.2.3 前 10 至 50 個感測器其精確率. 32
4.2.4 前 10 至 50 個感測器其召回率 33
4.2.5 模型分離與模型統一結果比較 34
4.2.6 全部對全部與多對一結果比較 36
4.2.7 PR curve 分數結果 38
4.2.8 正常感測器與異常感測器其巡檢當日的R2-score平均值 38
4.3 實驗環境介紹 40
五、結論與未來展望 41
5.1 結論 41
5.2 未來展望 42
參考文獻 44
附錄 A 實驗程式碼 47

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指導教授

陳弘軒(Hung-Hsuan Chen)

審核日期

2021-8-6

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