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. In real-life applications, however, demand for spare parts occurs randomly and fluctuates greatly, and the demand sequence shows obvious intermittent distribution characteristics. Additionally, due to factors such as reporting mistakes made by personnel or environmental changes, the actual data of the demand for spare parts are prone to abnormal variations. It is thus hard to capture the evolutional pattern of the demand for spare parts by traditional time series forecasting methods. The reliability of prediction results is also reduced. To address these concerns, this paper proposes a tensor optimization-based robust interval prediction method of intermittent time series for the aftersales demand for spare parts. First, using the advantages of tensor decomposition to effectively mine intrinsic information from raw data, a sequence-smoothing network based on tensor decomposition and a stacked autoencoder is proposed. Tucker decomposition is applied to the hidden features of the encoder, and the obtained core tensor is reconstructed through the decoder, thus allowing us to smooth outliers in the original demand sequence. An alternating optimization algorithm is further designed to find the optimal sequence feature representation and tensor decomposition factors for the extraction of the evolutionary trend of the intermittent series. Second, an adaptive interval prediction algorithm with a dynamic update mechanism is designed to obtain point prediction values and prediction intervals for the demand sequence, thereby improving the reliability of the forecast. The proposed method is validated using the actual aftersales data from a large engineering manufacturing enterprise in China. The experimental results demonstrate that, compared with typical time series prediction methods, the proposed method can effectively grab the evolutionary trend of various intermittent series and improve the accuracy of predictions made with small-sample intermittent series. Moreover, the proposed method provides a reliable elastic prediction interval when distortion occurs in the prediction results, offering a new solution for intelligent planning decisions related to spare parts in practical maintenance.

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. Secondly, the intermittent and temporal characteristics of the sequence are combined to construct a weight vector, and the learning of common information between domains is accomplished by weighting the distance of the output features of each cycle between domains. Finally, experiments are conducted on the actual after-sales datasets of two complex equipment manufacturing enterprises. Compared with various prediction methods, the method in this paper can effectively predict future demand trends, and the prediction's stability and accuracy are significantly improved.

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. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.

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. Specifically, we first propose a distributed framework of ECCoEAs, which consists of a communication mechanism, edge model management, and cloud model management. This communication mechanism is to avoid deadlock during the collaboration of edge servers and the cloud server. In edge model management, the edge models are trained based on local historical data and data composed of new solutions generated by co-evolutionary and their real evaluation values. In cloud model management, the black-box prediction functions received from edge models are used to find promising solutions to guide the edge model management. Moreover, two ECCoEAs are implemented, which proves the generality of the framework. To verify the performance of algorithms for distributed data-driven optimization problems, we design a novel benchmark test suite. The performance on the benchmarks and practical distributed clustering problems shows the effectiveness of ECCoEAs.

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. Firstly, the training data are transformed into multiple subspaces through the data enhancement technology. Then, a prototype clustering method is introduced to enhance the robustness of features representation under the framework of the robust deep auto-encoding algorithm. Finally, the robust multi-scale Deep-SVDD hyper sphere model is constructed to achieve online detection of abnormal state data. Experiments are conducted on the IEEE PHM Challenge 2012 bearing data set and XJTU-TU data set. The proposed method shows much greater susceptibility to incipient faults, and it has fewer false alarms. The robust multi-scale Deep-SVDD hyper sphere model significantly improves the performance of incipient fault detection for rolling bearings.

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. In this optimizer, a particle is updated only when its two exemplars randomly selected from the current swarm are its dominators. In this way, each particle has an implicit probability to directly enter the next generation, making it possible to maintain high swarm diversity. Since each updated particle only learns from its dominators, good convergence is likely to be achieved. To alleviate the sensitivity of this optimizer to newly introduced parameters, an adaptive parameter adjustment strategy is further designed based on the evolutionary information of particles at the individual level. Finally, extensive experiments on two high dimensional benchmark sets substantiate that the devised optimizer achieves competitive or even better performance in terms of solution quality, convergence speed, scalability, and computational cost, compared to several state-of-the-art methods. In particular, experimental results show that the proposed optimizer performs excellently on partially separable problems, especially partially separable multimodal problems, which are very common in real-world applications. In addition, the application to feature selection problems further demonstrates the effectiveness of this optimizer in tackling real-world problems.

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. First, a new robust state assessment method is presented. By introducing priori degradation information in the anomaly detection process of the isolated forest algorithm, this method can accurately assess the normal state and early fault state under noise interference. Second, a new deep domain adaptation algorithm is proposed. The algorithm uses the results of state assessment as output labels, and designs a deep domain adaptation neural network for joint adversarial training at feature level and model level simultaneously. Then a domain-invariant feature representation can be extracted from the data of different working conditions, and an online detection model can then be constructed. Comparative experiments are run on two bearing datasets IEEE PHM Challenge 2012 and XJTU-SY, and the results verifies the effectiveness of the proposed method in false alarm number and detection location.

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. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.

[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). The results showed that the same trend (BS > MS > US) was found for SMBC, SMBN, qMBC, qMBN, CAT and INV of upper soil layer, while a different trend (BS > US > MS) was observed for ALK. In addition, another trend (MS > US > BS) was observed for URE. The same trend (BS > MS >US) was observed for SMBN, qMBN, CAT, ALK, URE and INV in bottom layer, but a different trend (MS > BS > US) was observed for SMBC and qMBC. The SMBC, SMBN, CAT, ALK, URE and INV manifested as upper > bottom with reduction of the section, while qMBC and qMBN showed the opposite trend. Correlation analysis indicated that there were significant (P <0.05) or highly significant (P < 0.01) positive correlations among SMBC in different slope position and section, soil enzyme activity and moisture. According to the two equations of regression analysis, SMBC tended to increase with the increasing CAT and ALK, while decreased with the increasing pH. Then SMBN tended to increase with the increasing URE and 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 (0.86) > masson pine (0.67). Microbial groups and enzyme activity exhibited some similar responses to the litter decomposition process. After 135 days, fungal and microbial quantities reached the maximum while bacterial and actinomycetic number reached the minimum, presumably due to the high-temperature environment. The correlative analysis showed that the cellulase and acid phosphatase activity had significant positive relationship with the dry weight remaining rate (P < 0.05), which played a key role for microbes in utilizing the substrates at early stages. Meanwhile, the polyphenol oxidase activity showed highly significant negative correlation with the dry weight remaining rate (P < 0.01) in pine litter and the mixed litter, which worked on further decay of recalcitrant compound at late stages. Through the whole process, the microbial quantity and polyphenol oxidase activity were generally in the order of oak litter > mixed litter > pine litter, while in most cases the oak litter showed the lowest acid phosphatase activity, the ranking of which had some differences with the order of the decomposition constant K, indicating that litter decomposition was the result of integrated action by microbe and many kinds of enzymes. The results suggested that differences in litter composition and seasonal climate strongly influenced the microbial communities and the ecosystem processes they mediate. When mixed with oak leaves in given stand, the pine litter had an accelerating decomposition rate, which might depend on the higher microbial quantity and polyphenol oxidase activity in the mixed litter.