An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis

Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. However, convolution layers in CNN are very sensitive to the order of features, which can lead to instability in the processing of tabular data. Optimal order of features result in better performance of CNN models but it is expensive to seek such optimal order. In addition, because of the encapsulation mechanism of feature extraction, most CNN models are opaque and have poor interpretability, thus failing to identify root-cause features without human supervision. These difficulties inevitably limit the performance and credibility of CNN methods. In this paper, we propose an order-invariant and interpretable hierarchical dilated convolution neural network (HDLCNN), which is composed by feature clustering, dilated convolution and the shapley additive explanations (SHAP) method. The novelty of HDLCNN lies in its capability of processing tabular data with features of arbitrary order without seeking the optimal order, due to the ability to agglomerate correlated features of feature clustering and the large receptive field of dilated convolution. Then, the proposed method provides interpretability by including the SHAP values to quantify feature contribution. Therefore, the root-cause features can be identified as the features with the highest contribution. Computational experiments are conducted on the Tennessee Eastman chemical process benchmark dataset. Compared with the other methods, the proposed HDLCNN-SHAP method achieves better performance on processing tabular data with features of arbitrary order, detecting faults, and identifying the root-cause features

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become popular in the field of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a large number of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To address the aforementioned issues, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. The proposed SCCAM method is tested on a continuous stirred tank heater and the Tennessee Eastman industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis

Flow-field guided steering control for rigid autonomous ground vehicles in low-speed manoeuvring

This paper studies the low-speed manoeuvring problem for autono-mous ground vehicles operating in complex static environments. Making use of the intrinsic property of a fluid to naturally find its way to an outflow destination, a novel guidance method is proposed. In this approach, a reference flow field is calculated numerically through Computational Fluid Dynamics, based on which both the reference path topology and the steering reference to achieve the path are derived in a single process. Steering control considers three constraints: obstacle and boundary avoidance, rigidity of the vehicle, plus the non-holonomic velocity constraints due to the steering system. The influences of the parameters used during the flow field simulation and the control algorithm are discussed through numerical cases. A divergency field is defined to evaluate the quality of the flow field in guiding the vehicle. This is used to identify any problematic branching features of the flow, and control is adapted in the neighbourhood of such branching features to resolve possible ambiguities in the control reference. Results demonstrate the effectiveness of the method in finding smooth and feasible motion paths, even in complex environment

Transcriptome-wide identification and characterization of microRNAs in diverse phases of wood formation in Populus trichocarpa

We applied miRNA expression profiling method to Populus trichocarpa stems of the three developmental stages, primary stem (PS), transitional stem (TS), and secondary stem (SS), to investigate miRNA species and their regulation on lignocellulosic synthesis and related processes. We obtained 892, 872, and 882 known miRNAs and 1727, 1723, and 1597 novel miRNAs, from PS, TS, and SS, respectively. Comparisons of these miRNA species among different developmental stages led to the identification of 114, 306, and 152 differentially expressed miRNAs (DE-miRNAs), which had 921, 2639, and 2042 candidate target genes (CTGs) in the three respective stages of the same order. Correlation analysis revealed 47, 439, and 71 DE-miRNA-CTG pairs of high negative correlation in PS, TS, and SS, respectively. Through biological process analysis, we finally identified 34, 6, and 76 miRNA-CTG pairs from PS, TS, and SS, respectively, and the miRNA target genes in these pairs regulate or participate lignocellulosic biosynthesis-related biological processes: cell division and differentiation, cell wall modification, secondary cell wall biosynthesis, lignification, and programmed cell death processes. This is the first report on an integrated analysis of genome-wide mRNA and miRNA profilings during multiple phases of poplar stem development. Our analysis results imply that individual miRNAs modulate secondary growth and lignocellulosic biosynthesis through regulating transcription factors and lignocellulosic biosynthetic pathway genes, resulting in more dynamic promotion, suppression, or regulatory circuits. This study advanced our understanding of many individual miRNAs and their essential, diversified roles in the dynamic regulation of secondary growth in woody tree species

Biochar addition can negatively affect plant community performance when altering soil properties in saline-alkali wetlands

Biochar is a widely proposed solution for improving degraded soil in coastal wetland ecosystems. However, the impacts of biochar addition on the soil and plant communities in the wetland remains largely unknown. In this study, we conducted a greenhouse experiment using soil seed bank from a coastal saline-alkaline wetland. Three types of biochar, including Juglans regia biochar (JBC), Spartina alterniflora biochar (SBC) and Flaveria bidentis biochar (FBC), were added to the saline-alkaline soil at ratios of 1%, 3% and 5% (w/w). Our findings revealed that biochar addition significantly increased soil pH, and increased available potassium (AK) by 3.74% - 170.91%, while reduced soil salinity (expect for 3% SBC and 5%SBC) by 28.08% - 46.93%. Among the different biochar types, the application of 5% FBC was found to be the most effective in increasing nutrients and reducing salinity. Furthermore, biochar addition generally resulted in a decrease of 7.27% - 90.94% in species abundance, 17.26% - 61.21% in community height, 12.28% - 56.42% in stem diameter, 55.34% - 90.11% in total biomass and 29.22% - 78.55% in root tissue density (RTD). In particular, such negative effects was the worst in the SBC samples. However, 3% and 5% SBC increased specific root length (SRL) by 177.89% and 265.65%, and specific root surface area (SRSA) by 477.02% and 286.57%, respectively. The findings suggested that the plant community performance was primarily affected by soil pH, salinity and nutrients levels. Furthermore, biochar addition also influenced species diversity and functional diversity, ultimately affecting ecosystem stability. Therefore, it is important to consider the negative findings indirectly indicate the ecological risks associated with biochar addition in coastal salt-alkaline soils. Furthermore, Spartina alterniflora was needed to desalt before carbonization to prevent soil salinization when using S. alterniflora biochar, as it is a halophyte