Nowadays, the presence of many advanced sensors and actuators, the availability of different variables in a process, the increasing use of alarm systems to monitor the processes’performance, and thus improving these systems efficiency have become increasingly important due to the emergence of distributed control systems in industry. The alarm system alerts the operator when it faces with unusual conditions in the process or equipment. It clearly results in reducing the time in which each industry is suspended, increasing security, improving operator performance, and as a result it consolidates process performance. One of the other problems that an operator may have encountered is alarm flood. According to the existing standards of alarm system, each operator should not alert more than 10 times in every ten minutes. It could cause confusion and, as a result, the operator will delay and eventually may cause an inevitable catastrophe. This study aims at discussing the present problems in alarm system and in particular the huge number of alarms and its possible solutions. Among the existing methods’ shortcomings in the area of alarm flood some are prominent including being time-consuming and costly, non-susceptible to interferences and oscillations, lack of analyzing and comparing the floods in pairs and process information. Therefore, this paper has made an attempt to use actual industrial alarm data along with extracting information from them, creating unique alarm, and processing them in order to determine the time of alarm floods and their sequence and provide a procedure for preprocessing industrial alarm. Then, the given example will be solved manually considering data mining rocess and analysis and application a method called sequential pattern mining. Finally, these algorithms are applied automatically in data mining among the real industrial databases and their alarm flood sequences and examine their responses. We also will study the disorder effects on these algorithms. Key word: alarm system , alarm flood , data mining , sequential pattern mining.
