Influence of the combination of big data technology on the Spark platform with deep learning on elevator safety monitoring efficiency

Abstract

To effectively minimize elevator safety accidents, big data technology is combined with deep learning technology based on the Spark platform. This study first introduces the relevant theories of elevator safety monitoring technology, namely big data technology and deep learning technology. Then, the fault types that occur in the running state of the elevator are identified, and a finite state machine model is established. An elevator fault monitoring method based on the Spark platform is proposed, namely finite state machine (FSM), and the results of elevator safety fault monitoring are evaluated. Based on deep learning, an elevator fault warning model is constructed and its early warning performance is evaluated. The results show that the study can realize real-time and effective monitoring in the operation state of the elevator, and can determine the fault type of the elevator by binding the abnormal operation state with the corresponding fault. The feasibility of the elevator safety monitoring efficiency is evaluated based on three indexes: mutual information, accuracy, and false positives. Compared with other algorithms, the proposed FSM algorithm has the largest mutual information (0.1337), the highest accuracy (0.9899), the lowest false positive rate (0.0624), and the lowest false negative rate (0.1126); compared with other models, the elevator fault warning model proposed in this study has the lowest root mean-square error (RMSE) value (0.0201), the highest accuracy (0.9834), the lowest Loss value (0.0012), and the shortest convergence time (88.2608s), indicating that the elevator safety monitoring system and elevator fault warning model are feasible. This study establishes a good direction for elevator safety monitoring efficiency in China.

Exploration and Analysis of Collaborative Matching Algorithm Empowering Large Data Fiscal Budget

“…Ahmadi et al used the CART optimization algorithm to study the prediction performance and significance test of the short-term capital adequacy ratio of Chinese-listed companies [16]. Yu et al believe that the combination of professional equipment and big data technology can provide a good help for professional budget analysis [17]. Koyuncugil proposed a financial risk early warning system model based on data mining [18].…”

Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms

“…Yu et al (2020) combined big data technology with deep learning technology, proposed a method of elevator fault monitoring based on the Spark platform, and constructed an elevator fault warning model. This research enables real-time effective monitoring of the elevator operation status and fault type determination [12]. Chai et al (2021) proposed a non-intrusive artificial intelligence (AI)-based diagnostic system that employs a Multivariate Long-and Short-Term Memory Fully Convolutional Network (MLSTM-FCN) to learn and analyze measurement signals from a non-intrusive inspection system of an elevator.…”

Classification and pattern extraction of incidents: a deep learning-based approach

“…DNN, proposed in the early 1980s [44] and revamped in 2002 [45], faced training difficulties initially in deep architectures. Later, it was used in a broad spectrum of application areas, including fraud detection [40], dynamic planning of public bicycle-sharing system [46], time series prediction [47], Spark-based computation [48,49], pattern recognition [50], speech recognition [51], classification [52,53], image processing [54,55], and video processing [41]. The main characteristic of this approach is that it can show better classification performance in the case of analysis of complex and a large amount of data [56][57][58].…”