The recent proliferation of Internet of Things (IoT) sensors has driven a myriad of industrial and urban applications. Through analyzing massive data collected by these sensors, the proactive maintenance management can be achieved such that the maintenance schedule of the installed equipment can be optimized. Despite recent progress in proactive maintenance management in industrial scenarios, there are few studies on proactive maintenance management in urban informatics. In this article, we present an integrated framework of IoT and cloud computing platform for the proactive maintenance management in smart city. Our framework consists of: 1) an IoT monitoring system for collecting time-series data of operating and ambient conditions of the equipment and 2) a hybrid deep learning model, namely, convolutional bidirectional long short-term memory (CBLM) model for forecasting the operating and ambient conditions based on the collected time-series data. In addition, we also develop a naïve Bayes classifier to detect abnormal operating and ambient conditions and assist management personnel in scheduling maintenance tasks. To evaluate our framework, we deployed the IoT system in a Hong Kong public toilet, which is the first application of proactive maintenance management for a public hygiene and sanitary facility to the best of our knowledge. We collected the sensed data more than 33 days (808 h) in this real system. Extensive experiments on the collected data demonstrated that our proposed CBLM outperformed six traditional machine learning algorithms.

Feature recognition is an important technology of computer-aided design/computer-aided engineering/computer-aided process planning/computer-aided manufacturing integration in cast-then-machined part manufacturing. Graph-based approach is one of the most popular feature recognition methods; however, it cannot still solve concave-convex mixed interacting feature recognition problem, which is a common problem in feature recognition of cast-then-machined parts. In this study, an oriented feature extraction and recognition approach is proposed for concave-convex mixed interacting features. The method first extracts predefined features directionally according to the rules generated from attributed adjacency graphs–based feature library and peels off them from part model layer by layer. Sub-features in an interacting feature are associated via hints and organized as a feature tree. The time cost is reduced to less than [Formula: see text] by eliminating subgraph isomorphism and matching operations. Oriented feature extraction and recognition approach recognizes non-freeform-surface features directionally regardless of the part structure. Hence, its application scope can be extended to multiple kinds of non-freeform-surface parts by customizing. Based on our findings, implementations on prismatic, plate, fork, axlebox, linkage, and cast-then-machined parts prove that the proposed approach is applicable on non-freeform-surface parts and effectively recognize concave-convex mixed interacting feature in various mechanical parts.

Roughing tool path of panel machining, which is a bottleneck of spacecraft production, should be optimised rapidly to shorten process time. This problem has a large solution space, and surface quality should be taken into account. The decision variables are cavity machining order, feed point and cutting direction of each cavity. Our problem is presented as an asymmetric general travelling salesman problem (AGTSP). A cluster optimisation-based hybrid max-tmin ant system (CO-HMMAS) is proposed, which solves two sub-problems as a whole. The oriented pheromone and dynamic heuristic information calculating methods are designed. We analyse the differences between one-stage and two-stage AGTSP local search heuristics and combine CO-HMMAS with them properly. An improved Global 3-opt heuristic suitable for both symmetric and asymmetric cases is proposed with sharply reduced time complexity. Comparison experiments verified that, two-stage local search heuristics decrease solution error significantly and rapidly when the error is great, and one-stage ones improve a near-optimal solution costing much more computing time. Benchmarks tests show that, CO-HMMAS outperforms the state-of-the-art algorithm on several technical indexes. Experiments on typical panels reveal that all algorithm improvements are effective, and CO-HMMAS can obtain a better tool path than the best algorithm within less CPU time.

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.

Accurate prediction of cycle time (CT) plays an important role in the promises of a good delivery-time for semiconductor manufacturers. However, the CTs of wafer lots are difficult to forecast since they are influent by a lot factors (e.g., workload for every machine). The identification of key factors (called CT-related) can not only improve the prediction performance but also facilitate the control of CT, which is of particular significance. This paper focuses on gathering subtle variables into the candidate variables set along with the further analysis which is required in the correlation analysis between various candidate factors and CTs of wafer lots. The candidates set is first constructed to collect all factors that may affect wafer lots' CT. Then, a regression-based model is proposed to select CT-related variables, which consists of three parts: 1) discretization; 2) adaptive logistic regression-based correlation analysis; and 3) CT-related factor selection. Subsequently, a parallel computation model is implemented to forecast the CTs of wafer lots. In the numerical experiments, 108 CT-related factors stood out from 774 candidates and replaced six global factors (used as reference) to predict CTs of wafer lots. The results indicated that the proposed approach had higher accuracy than linear regression and back propagation network in CT forecasting in large scale data analysis.

Forecasting short-term cycle time (CT) of wafer lots is crucial for production planning and control in the wafer manufacturing. A novel recurrent neural network called “bilateral long short-term memory (bilateral LSTM)” is proposed to model a short-term cycle time forecasting (CTF) of each re-entrant period of a wafer lot. First, a two-dimensional (2-D) architecture is designed to transmit the wafer and layer correlations by using wafer and layer connections. Subsequently, aiming to store various error signals caused by the diverse CT data, a multiply memory structure is presented to extend the capacity of constant error carousel (CEC) in the LSTM model. The experiment results indicate that the proposed model outperforms conventional models in the accuracy and stability for the short-term CTF. Further comparative experiments reveal that the 2-D architecture can enhance the prediction accuracy and the multi-CEC structure can improve the forecasting stability for the short-term CTF of wafer lots.

Production scheduling problems in re-entrant manufacturing systems are complex due to their features of large-scale complexity, unbalanced workload and dynamic uncertainty. The aim of this paper is thus to develop an effective way of formulating production schedules for designing RMSs. First, a multi-agent-based hierarchical collaborative system consisting of a system layer, a machine layer, and a material handling device layer is developed to improve the efficiency of RMSs. The objective of the system layer is to maximise the total processing profit, and the objective of the machine layer is to determine the winning bid. Second, a contract net protocol scheduling algorithm is applied to solve capacity planning problems for key machine groups in the system layer. Third, a generalised partial global planning-contract net collaborative mechanism is adopted to allocate tasks to machines within each machine group in the machine layer. Finally, the performance of the proposed approach is validated through a case study, and the results demonstrate that the proposed approach outperforms the first-come first-serve rule in combination with the minimised batch size rule in terms of daily movement and machine utilisation.