Abstract Small load carriers (SLCs) are standardized reusable containers used to transport and protect customer goods in many manufacturers. Throughout the life cycle of the SLCs, they will be collected, manually checked for defects (wear, cracks, and residue on the surface), and cleaned by specialized logistic companies. Human operators in small to medium-sized companies manually evaluate the defects due to the variety and degree of possible defects and varying customer needs. This manual evaluation is not scalable and prone to errors. This work aims to fill this gap by proposing a computer vision system that can recognize the SLC type for inventory management and perform defect detection automatically. First, we develop a camera portal, consisting of standard components, that capture the relevant surfaces of the SLC. A labeled dataset of 17,530 images of 34 different SLCs with their defect status was recorded using this camera portal. We trained a classification model (ConvNeXt) using our dataset to predict the different types of SLCs achieving 100% class prediction accuracy. For defect detection, we explore eight state-of-the-art (SOTA) anomaly detection models that achieved high rankings in the MVTec industrial anomaly detection benchmark. These models are trained using default hyperparameters and the two highest-scoring models were chosen and fine-tuned. The best-fine-tuned models based on “Area under the Receiver Operating Characteristic Curve (AUROC)” are PatchCore (0.811) and DRAEM (0.748). These results indicate that there is still potential for improvement in the automation of defect detection of SLCs.

Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems.As current FID methods are based on chain of thought without considering dependencies among target classes.To capture and explore dependencies, we propose a hierarchical knowledge guided fault intensity diagnosis framework (HKG) inspired by the tree of thought, which is amenable to any representation learning methods.The HKG uses graph convolutional networks to map the hierarchical topological graph of class representations into a set of interdependent global hierarchical classifiers, where each node is denoted by word embeddings of a class.These global hierarchical classifiers are applied to learned deep features extracted by representation learning, allowing the entire model to be end-toend learnable.In addition, we develop a re-weighted hierarchical knowledge correlation matrix (Re-HKCM) scheme by embedding inter-class hierarchical knowledge into a data-driven statistical correlation matrix (SCM) which effectively guides the information sharing of nodes in graphical convolutional neural networks and avoids over-smoothing issues.The Re-HKCM is derived from the

Anomalous sound detection (ASD) is one of the most significant tasks of mechanical equipment monitoring and maintaining in complex industrial systems. In practice, it is vital to efficiently identify abnormal status of the working mechanical system, which can further facilitate the failure troubleshooting. In this paper, we propose a multi-pattern adversarial learning one-class classification framework, which allows us to use both the generator and the discriminator of an adversarial model for efficient ASD. The core idea is to learn reconstructing the normal patterns of acoustic data through two different patterns from auto-encoding generators, which succeeds in generalizing the fundamental role of a discriminator from identifying real and fake data to distinguishing between regional and local pattern reconstructions. Moreover, we design a novel balanceable detection strategy using both generators and a discriminator to achieve anomaly detection efficiently. Furthermore, we present a global filter layer for long-term interactions in the frequency domain space, which directly learns from the original data without introducing any human priors. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG, and one existing publicly) for anomaly detection, all showing superior results and outperform recent state-of-the-art ASD methods.

This paper contributes a framework that helps to fulfill the requirements of the standards DIN EN 16247 and ISO 50001 by combining (i) a synchronized data acquisition, (ii) data integration, (iii) learning of normal behavior models and (iv) a implementation of an anomaly detection as prototype. Both standards require a reliable data acquisition and energy consumption analysis for implementing a certified energy management system. It shows that this framework meets the specifications of the standards by implementing a combined data acquisition and anomaly detection approach.