Improved correlation analysis and visualization of industrial alarm data

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

Data-driven approach for time-delay estimation of industrial processes

An accurate estimate of time delay is of crucial importance for control tasks such as performance assessment and controller design. In this paper, a novel data-driven approach to time-delay estimation is developed for a process subject to industrial background disturbances, in which the closed-loop output data under the routine operating conditions is only required. The practical solutions are proposed to estimate the time delay based on the estimated impulse response of the closed loop that is estimated online by utilizing the output data. Without relying on system identification and any prior knowledge of the process as well, the time delay is estimated directly for a large time delayed process; while for a small time delayed process, the time delay is estimated by means of the stationarilized filter, the pre-filter and the loop filter. The effectiveness of the proposed approach is validated by various numerical and industrial examples including a distillation column, a petroleum refinery heating furnace and a ceramic dryer.

Adaptive baseline model for autonomous marine equipment and systems

With the rapid development of the Internet of Things (IoT) and the Fourth Industrial Revolution, marine equipment and systems are becoming increasingly automated and autonomous. Judging the status of equipment and systems for autonomous shipping assumes that the benchmark of status evaluation is not easily obtained, and the performance baseline for the benchmark is usually static and cannot be accurately adapted under dynamic operating conditions. This paper deals with the issue of establishing a baseline for marine equipment and systems by using a data-driven method. Considering the working conditions of marine equipment and systems, a reference-site (R-S) model was first proposed to determine the initial baseline. This method could solve the problem of inadequate parameters in the initial state very well. Then, a dynamic kernel (D-K) model was used to increase the number of reference sites and update the reference points. This method reduced the amount of data calculation in the process of a dynamic update of the baseline. Continuously fitting the reference points enabled the dynamically updated performance baseline to cope with the working conditions. To implement the proposed method, the index parameters in the R-S and D-K models were processed, and the sliding window capacity was determined using the Kolmogorov-Smirnov method. Finally, the proposed baseline model was applied to a practical case of the exhaust temperature of a marine diesel engine. The result revealed that the proposed method yielded a more accurate baseline and better adaptive performance.

A novel detection of correlated alarms with delays based on improved block matching similarities

Statistical analysis method has emerged as a general approach to detect relation alarms in the process industries. However, the delay between related alarms is the main cause leading to wrong analysis results from traditional approaches to detect correlated alarms. This paper proposed a novel detection of correlated alarms based on block matching similarities with delay (BMS-d). First, blocking alarm data sequence method is to transform alarm data into time node sequences, which is able to reduce the calculation burden of the correlation analysis. Second, a novel maximal block correlation coefficient method is presented to estimate the correlation delay between alarms. Third, a novel method is proposed to detect correlated alarms based on the block matching similarities and related alarm delay information. A numerical case and TE process are employed to demonstrate the effectiveness and efficiency of the proposed method.

Performance evaluation and design for variable threshold alarm systems through semi-Markov process

In large industrial systems, alarm management is one of the most important issues to improve the safety and efficiency of systems in practice. Operators of such systems often have to deal with a numerous number of simultaneous alarms. Different kinds of thresholding or filtration are applied to decrease alarm nuisance and improve performance indices, such as Averaged Alarm Delay (ADD), Missed Alarm and False Alarm Rates (MAR and FAR). Among threshold-based approaches, variable thresholding methods are well-known for reducing the alarm nuisance and improving the performance of the alarm system. However, the literature suffers from the lack of an appropriate method to assess performance parameters of Variable Threshold Alarm Systems (VTASs). This study introduces two types of variable thresholding and proposes a novel approach for performance assessment of VTASs using Priority-AND gate and semi-Markov process. Application of semi-Markov process allows the proposed approach to consider industrial measurements with non-Gaussian distributions. In addition, the paper provides a genetic algorithm based optimized design process for optimal parameter setting to improve performance indices. The effectiveness of the proposed approach is illustrated via three numerical examples and through a comparison with previous studies.

Process alarm prediction using deep learning and word embedding methods

Industrial alarm systems play an essential role for the safe management of process operations. With the increase in automation and instrumentation of modern process plants, the number of alarms that the operators manage has also increased significantly. The operators are expected to make critical decisions in the presence of flooding alarms, poorly configured and maintained alarms and many nuisance alarms. In this environment, if the incoming alarms can be correctly predicted before they actually occur, the operators may have a chance to address and possibly avoid abnormal behaviors by taking corrective actions in time. Inspired by the application of deep learning in natural language processing, this paper presents an alarm prediction method based on word embedding and recurrent neural networks to predict the next alarm in a process setting. This represents both a novel approach to alarm management as well as a novel application of natural language processing and deep learning techniques to this problem. The proposed method is applied to an actual case study to demonstrate its performance.

Lightweight monitoring and control system for coal mine safety using REST style

The complex environment of a coal mine requires the underground environment, devices and miners to be constantly monitored to ensure safe coal production. However, existing coal mines do not meet these coverage requirements because blind spots occur when using a wired network. In this paper, we develop a Web-based, lightweight remote monitoring and control platform using a wireless sensor network (WSN) with the REST style to collect temperature, humidity and methane concentration data in a coal mine using sensor nodes. This platform also collects information on personnel positions inside the mine. We implement a RESTful application programming interface (API) that provides access to underground sensors and instruments through the Web such that underground coal mine physical devices can be easily interfaced to remote monitoring and control applications. We also implement three different scenarios for Web-based, lightweight remote monitoring and control of coal mine safety and measure and analyze the system performance. Finally, we present the conclusions from this study and discuss future work.