Smart-connected technologies, such as the Internet of things and cloud computing, are transforming how industries and enterprises do business by improving the lifecycle management of their product. Therefore, to study the lifecycle management of complex products under smart-connected environment, many studies have been published outlining new methods for smart development, intelligent manufacturing, and data-driven services. This paper first proposes the concept and architecture of smart-connected products and then systematically reviews the current research about new paradigms of product development, manufacturing and services. The paper also summarises the conceptual framework, research issues and opportunities of each phase of the product lifecycle management.

The task personalized recommendation problems in software crowdsourcing systems have unique characteristics, i.e., large task flow, high task complexity, long development cycle, winning task competitions, professional ability requirements, etc. However, existing software crowdsourcing recommendation mechanisms do not consider the contextual information of crowdsourcing tasks. In particular, the effects of crowdsourcing workers' interest changes and capability constraints on task selection are usually ignored. Therefore, this study proposes a new worker capability-correction long- and short-term attention network (CLSAN) recommendation framework. Firstly, explicit and implicit features are obtained from the feature data of crowdsourcing workers and historical crowdsourcing tasks. The long-term and short-term feature layers are extracted by adding LSTM to the historical tasks; the attention weight of user preference is calculated by integrating the attention mechanism to obtain each user's personalized preference. Secondly, this study considers that the capabilities of the worker need to be matched with the skills required for the software task. We design a worker capability correction model that uses Word2Vec software to obtain competency similarities between crowdsourcing workers and tasks, thereby determining the priority of tasks that satisfy the worker's capability. The experimental results show that CLSAN can accurately evaluate the changes of interest preferences of crowdsourcing workers, and effectively improve the quality and efficiency of crowdsourcing recommendations.

Convolutional neural networks (CNNs) have been widely used in fault diagnosis due to their superiority in feature extraction. Traditional CNNs are a type of closed-box techniques with little interpretability, and their effectiveness is greatly affected when fault mechanisms and modes are extremely complex. To cope with such issue, this article presents a way to integrate outlier-type prior knowledge into CNNs based on an attention mechanism for fault diagnosis. First, outliers of the image-like data obtained by a sliding window processing from the raw data are formally defined as prior knowledge. Then, the defined outlier-type prior knowledge is integrated into any layer of CNNs by a parameter-free attention mechanism. Compared with existing similar methods, the proposal realizes a novel and flexible definition of prior knowledge and achieves deep fusion of prior knowledge and CNNs with low computational cost. The performance of the proposal was evaluated on the Tennessee Eastman process dataset and the real wind turbine blade icing dataset, which indicates that the proposal could not only realize accurate results but also had good model interpretability in terms of achieving high accuracy. The acquisition of outlier-type prior knowledge was discussed and the results demonstrate the effectiveness of the proposed prior knowledge integration method.

Efficient service scheduling is an important technique supporting collaborative manufacturing platforms such as cloud manufacturing. To achieve a more efficient task execution, heavy-duty equipment manufacturing, an important field of cloud manufacturing, must be explored beyond parameters of cost and time. The manufacturing service composition problem of heavy-duty equipment has the characteristics of task complexity, high process energy consumption, and multi-granularity nature of service (MGNoS). In the manufacturing process of heavy-duty equipment, the energy consumption of required logistics accounts for 30% the total energy consumption. However, to date, research has investigated the problem almost always from the task level, and MGNoS has received little attentions, which may lead to redundant energy consumption in logistics during manufacturing execution. In this paper, the problem of manufacturing service scheduling with integrating energy saving and service composition in cloud manufacturing is considered. Based on the mathematical description, a cross-granularity task chain reconfiguration algorithm is presented for mitigating the adverse effects of MGNoS and developing the adaptive non-dominated sorting genetic algorithm Ⅲ for solving the service composition scheme to generate optimal scheduling solutions. The effectiveness and efficiency performances of typical optimization algorithms are compared with the proposed approach. The results show that the proposed method achieves significant energy savings for all tasks in different scenarios.