Controller area network(CAN) based fieldbus technologies have been widely used in networked manufacturing systems. As the information channel of the system, the reliability of the network is crucial to the system throughput, product quality, and work crew safety. However, due to the inaccessibility of the nodes’ internal states, direct assessment of the reliability of CAN nodes using the nodes’ internal error counters is infeasible. In this paper, a novel CAN node reliability assessment method, which uses node’s time to bus-off as the reliability measure, is proposed. The method estimates the transmit error counter(TEC) of any node in the network based on the network error log and the information provided by the observable nodes whose error counters are accessible.First, a node TEC estimation model is established based on segmented Markov chains. It considers the sparseness of the distribution of the CAN network errors. Second, by learning the differences between the model estimates and the actual values from the observable node, a Bayesian network is developed for the estimation updating mechanism of the observable nodes. Then, this estimation updating mechanism is transferred to general CAN nodes with no TEC value accessibility to update the TEC estimation. Finally, a node reliability assessment method is developed to predict the time to reach bus-off state of the nodes. Case studies are carried out to demonstrate the effectiveness of the proposed methodology. Experimental results show that the estimates using the proposed model agree well with actual observations. As the information channel of the system, the reliability of the network is crucial to the system throughput, product quality, and work crew safety. However, due to the inaccessibility of the nodes ’internal states, direct assessment of the reliability of CAN nodes using the nodes’ internal error counters is infeasible. In this paper, a novel CAN node reliability assessment method, which uses node’s time to bus-off as the reliability measure, is proposed. The method estimates the transmit error counter (TEC) of any node in the network based on the network error log and the information provided by the observable nodes whose error counters are accessible. First, a node TEC estimation model is established based on segmented Markov chains. It considers the sparseness of the distribution of the CAN network errors. Second, by learning the differences between the model estimates an d the actual values ​​from the observable node, a Bayesian network is developed for the estimation updating mechanism of the observable nodes. Finally, a node results assessment method is developed to predict the time to reach bus-off state of the nodes.