Robust Interval Prediction of Intermittent Demand for Spare Parts Based on Tensor Optimization.

Demand for spare parts, which is triggered by element failure, project schedule and reliability demand, etc., is a kind of sensing data to the aftermarket service of large manufacturing enterprises. Prediction of the demand for spare parts plays a crucial role in inventory management and lifecycle quality management for the aftermarket service of large-scale manufacturing enterprises.

Spare Parts Demand Forecasting Method Based on Intermittent Feature Adaptation.

The demand for complex equipment aftermarket parts is mostly sporadic, showing typical intermittent characteristics as a whole, resulting in the evolution law of a single demand series having insufficient information, which restricts the prediction effect of existing methods. To solve this problem, this paper proposes a prediction method of intermittent feature adaptation from the perspective of transfer learning. Firstly, to extract the intermittent features of the demand series, an intermittent time series domain partitioning algorithm is proposed by mining the demand occurrence time and demand interval information in the series, then constructing the metrics, and using a hierarchical clustering algorithm to divide all the series into different sub-source domains.

Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining.

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series.

Edge-Cloud Co-Evolutionary Algorithms for Distributed Data-Driven Optimization Problems.

Surrogate-assisted evolutionary algorithms (EAs) have been proposed in recent years to solve data-driven optimization problems. Most existing surrogate-assisted EAs are for centralized optimization and do not take into account the challenges brought by the distribution of data at the edge of networks in the era of the Internet of Things. To this end, we propose edge-cloud co-EAs (ECCoEAs) to solve distributed data-driven optimization problems, where data are collected by edge servers.

The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings.

Aiming at the online detection problem of rolling bearings, the limited amount of target bearing data leads to insufficient model in training and feature representation. It is difficult for the online detection model to construct an accurate decision boundary. To solve the problem, a multi-scale robust anomaly detection method based on data enhancement technology is proposed in this paper.

A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault.

For online early fault detection of rolling bearings in non-stop scenarios, one of the main concerns is the model bias caused by the distribution shift between offline and online working conditions. Under such concern, how to improve the feature sensitivity to early faults and the robustness of detection model has become a key challenge of improving the effectiveness of online detection. To solve this problem, a new online early fault detection method is proposed in this paper based on a strategy of deep transfer learning.

A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault.

With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness.

An Adaptive Stochastic Dominant Learning Swarm Optimizer for High-Dimensional Optimization.

High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such problems with high effectiveness and efficiency, this article proposes a simple yet efficient stochastic dominant learning swarm optimizer. Particularly, this optimizer not only compromises swarm diversity and convergence speed properly, but also consumes as little computing time and space as possible to locate the optima.

[Influence of different slope position and profile in Disporopsis pernyi forest land on soil microbial biomass and enzyme activity in southwest Karst mountain of China ].

Soil microbial biomass and enzyme activity are important parameters to evaluate the quality of the soil environment. The goal of this study was to determine the influence of different slope position and section in Disporopsis pernyi forest land on the soil microbial biomass and enzyme activity in southwest Karst Mountain. In this study, we chose the Dip forest land at Yunfo village Chengdong town Liangping country Chongqing Province as the study object, to analyze the influence of three different slope positions [Up Slope(US), Middle Slope(MS), Below Slope(BS)] and two different sections-upper layer(0-15 cm) and bottom layer(15-30 cm) on the soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), microbial carbon entropy (qMBC), microbial nitrogen entropy (qMBN) , catalase(CAT), alkaline phosphatase (ALK), urease(URE), and invertase(INV).

[Dynamics of microbes and enzyme activities during litter decomposition of Pinus massoniana forest in mid-subtropical area].

The dynamics of microbial quantity and enzyme activities during decomposition process of masson pine (Pinus massoniana) leaf litter, oak (Quercus aliena) leaf litter and their mixture (at natural mass ratio, 8: 2) were studied with litterbag method in the pinus forest typical vegetations of mid-subtropical Jinyun Mountain nature reserve. The results showed that the decomposition constant K of leaf litter ranked as follows: mixture (0.94) > oak (0.