| dc.description.abstract | The right maintenance strategy is vital to achieve cost-effective maintenance over the entire asset lifetime. Industry 4.0 and the corresponding technologies e.g. advanced robots have revolutionised the production processes and maintenance as well. One of the modern emerging technologies is the use of advanced robots in smart warehousing systems, and AutoStore AS has become a pioneer in that. AutoStore offers to their customers a smart warehousing system that utilise robots to store, move, pick, and manage their items in a more efficient manner and less spare occupying than with traditional warehousing.
However, since the AutoStore system are quite new and were implemented in several different sites, several errors has emerged over the last couple of years. The error estimations have proven to be unacceptable with some unexpected failures and more replacements done than what was estimated. AutoStore would like to see if a preventive maintenance program can be a better and more reliable program for them to reduce maintenance cost in a lifetime and stabilize the uptime in the systems. Therefore, Autostore think it is timely to study the errors data and determine the right maintenance action for each critical error. Fortunately, Autostore systems have advanced control and data collection systems called logfiles that collect information about errors. It has based on input information, knowledge and logfiles been set up an estimated maintenance program that are used by the distributors and customers today.
Thus, the purpose of this thesis is to analyse the error database and determine the right maintenance action to eliminate or monitor the cause behind those errors.
In order to achieve the desired goal of the thesis, the error database was systemically analysed to determine the critical site, critical systems within the entire warehousing system and the critical errors. Later, the pattern in the error occurrence over the time was analysed using Weibull method. Finally, the recommended maintenance actions were proposed. The entire systems analysis and associated cost analysis were performed through a case study related to DHL-TI Singapore and specifically focused on robot type 5.
By using the developed method, error data was analysed, and the related cost analyses conducted. The most critical errors were defined through the analysed data and defined in relation to cost and stops in the facility.
The data turned out to be inconclusive. Due to a short analyses interval, too early in the life cycle of the selected system and too many updates and enhancements cannot the historical data conclude in a specific maintenance program. A longer future analyse interval is suggested, also to enhance the learning outcomes from the individual learning approach that are existing in practice today, to a more organizational learning approach. Service personnel is encouraged to share their experienced so that a holistic learning approach can be achieved.
Some solutions were suggested to the described problem, and it was suggested to continue with the historical data analyses in the future years and to use real-time data by using condition monitoring to predict and act on occurring errors based on actual, real-time data. Common for all solutions are that the root causes for the critical errors must be known. There is too little knowledge about what are causing the different errors, and that is reflected by the inconclusive results. By using more time and resources to root cause - analyses will a better understanding of the error be achieved, and that way can a better and more accurate maintenance strategy be set up, that are taking a basis in the actual condition of the robot and the related components. | nb_NO |
