Ship Block State Identification and Transfer Monitoring Based on Time-Site Data of Blocks

Abstract

Ship block transfer is important to the orderly flow of blocks between crafts, which is costly. Shipyard managers have to monitor the actual transfers, especially the unproductive transfers that occur when blocks are obstructed or reworked. A high-load shipyard, called S, often uses one site for multiple purposes, and the difficulties in obtaining the state of ship blocks through the time-site data of blocks provided by the existing monitoring technology make it difficult to monitor two types of unproductive transfers. To address this problem, four hidden Markov models whose parameters are calculated by a supervised approach are proposed, and a Viterbi algorithm based method is proposed to identify the state of blocks, achieving an accuracy of up to 93.5% on the test dataset. One of the hidden Markov models is applied to the time-site data of blocks to monitor two types of unproductive transfers in shipyards. Preliminary suggestions for improving the blocks transfer process based on monitoring results are proposed.

Keywords

• ship block transfer
• block state identification
• block transfer monitoring
• hidden markov model (hmm)