In the manufacturing process, sensor data collected from equipment is crucial for building predictive models to manage processes and improve productivity. However, in the field, it is challenging to gather sufficient data to build robust models. This study proposes a novel predictive model based on the Transformer, utilizing statistical feature embedding and window positional encoding. Statistical features provide an effective representation of sensor data, and the embedding enables the Transformer to learn both time- and sensor-related information. Window positional encoding captures precise time details from the feature embedding. The model's performance is evaluated in two problems: fault detection and virtual metrology, showing superior results compared to baseline models. This improvement is attributed to the efficient use of parameters, which is particularly beneficial for sensor data that often has limited sample sizes. The results support the model's applicability across various manufacturing industries, demonstrating its potential for enhancing process management and yield.

Root Cause Analysis (RCA) in the manufacturing of electric vehicles is the process of identifying fault causes. Traditionally, the RCA is conducted manually, relying on process expert knowledge. Meanwhile, sensor networks collect significant amounts of data in the manufacturing process. Using this data for RCA makes it more efficient. However, purely data-driven methods like Causal Bayesian Networks have problems scaling to large-scale, real-world manufacturing processes due to the vast amount of potential cause-effect relationships (CERs). Furthermore, purely data-driven methods have the potential to leave out already known CERs or to learn spurious CERs. The paper contributes by proposing an interactive and intelligent RCA tool that combines expert knowledge of an electric vehicle manufacturing process and a data-driven machine learning method. It uses reasoning over a large-scale Knowledge Graph of the manufacturing process while learning a Causal Bayesian Network. In addition, an Interactive User Interface enables a process expert to give feedback to the root cause graph by adding and removing information to the Knowledge Graph. The interactive and intelligent RCA tool reduces the learning time of the Causal Bayesian Network while decreasing the number of spurious CERs. Thus, the interactive and intelligent RCA tool closes the feedback loop between expert and machine learning method.

Many industrial sectors have been collecting big sensor data. With recent technologies for processing big data, companies can exploit this for automatic failure detection and prevention. We propose the first completely automated method for failure analysis, machine-learning fault trees from raw observational data with continuous variables. Our method scales well and is tested on a real-world, five-year dataset of domestic heater operations in The Netherlands, with 31 million unique heater-day readings, each containing 27 sensor and 11 failure variables. Our method builds on two previous procedures: the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning algorithm from Boolean data. C4.5 pre-processes each continuous variable: it learns an optimal numerical threshold which distinguishes between faulty and normal operation of the top-level system. These thresholds discretise the variables, thus allowing LIFT to learn fault trees which model the root failure mechanisms of the system and are explainable. We obtain fault trees for the 11 failure variables, and evaluate them in two ways: quantitatively, with a significance score, and qualitatively, with domain specialists. Some of the fault trees learnt have almost maximum significance (above 0.95), while others have medium-to-low significance (around 0.30), reflecting the difficulty of learning from big, noisy, real-world sensor data. The domain specialists confirm that the fault trees model meaningful relationships among the variables.

We present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particular, the method allows one to produce CPTs for nodes involved in the reversal that exploit regularities in the conditional distributions. We argue that this approach alleviates some of the overhead associated with arc reversal, plays an important role in evidence integration and can be used to restrict sampling of variables in DPNs. We also provide an algorithm that detects the dynamic irrelevance of state variables in forward simulation. This algorithm exploits the structured CPTs in a reversed network to determine, in a time-independent fashion, the conditions under which a variable does or does not need to be sampled.