Dynamic Alarm Research Articles

A multi-head self-attention autoencoder network for fault detection of wind turbine gearboxes under random loads*

Wind turbine gearboxes work under random load for extended periods of time, and the fault detection indicator constructed by the existing deep learning models fluctuate constantly due to the load, which is easy to cause frequent false alarms. Therefore, a multihead self-attention autoencoder network is proposed and combined with a dynamic alarm threshold to detect faults in a wind turbine gearbox subjected to random loads. The multiheaded attention mechanism layer enhances the feature-extraction capability of the proposed network by extracting global and local features from input data. Furthermore, to suppress the influence of the random load, a dynamic warning threshold was designed based on the reconstruction error between the inputs and outputs of the proposed network. Finally, the effectiveness of the proposed method was verified using the vibration data of wind turbine gearboxes from an actual wind farm.

Multivariate Alarm Monitoring for Non-Convex Normal Operating Zones Based on Search Cones

Normal operating zones (NOZs) are the variation spaces of multiple related variables under normal conditions. This paper proposes a search-cone-based method to establish NOZ models and design dynamic alarm thresholds for multivariate alarm monitoring. First, mathematical models are established by exploiting search cones from historical normal data points to describe non-convex NOZs of multiple related process variables. Second, dynamic alarm thresholds of each process variable are designed based on the established NOZ models for new data points being inside or outside the NOZs. The proposed method extends from convex NOZs to non-convex ones, and the main contribution is to establish mathematical models of NOZs for monitoring abnormal conditions and design dynamic alarm thresholds for generating alarms. Continuous stirred tank reactors (CSTR) and industrial examples are provided to illustrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Alarm systems play critically important roles for safe and efficient operation of industrial plants. Many process variables are related so that their normal variation ranges formulate high-dimensional geometric spaces referred to as normal operating zones (NOZs). Hence, it is desirable to adopt the NOZs for alarm monitoring. By contrast, if relationships among process variables are ignored, missed and false alarms may occur to deteriorate the performance of alarm systems. This paper proposes a method to establish mathematical models for NOZs and design dynamic alarm thresholds. The established models can describe non-convex or convex NOZs; the dynamic alarm thresholds can quantify distances between NOZ boundaries and operating points of a multivariate process

An Integrated Framework via Spectrum Sparsity Measure and Dynamic Alarm Thresholds for Online Fault Detection

An Integrated Framework via Spectrum Sparsity Measure and Dynamic Alarm Thresholds for Online Fault Detection

A priority-aware sequential pattern mining method for detection of compact patterns from alarm floods

Alarm floods are commonly present in modern complex industrial process, and usually degrade the performance of the alarm systems severely. In an alarm flood, the massive alarm messages may overwhelm plant operators and submerge critical alarms, which would further worsen the situation and lead to severe consequences. One effective way to deal with alarm flood situations is to mine meaningful alarm patterns that can be used to configure dynamic alarm suppression modules that suppress irrelevant alarms in alarm floods based on certain conditions or to help with online alarm prediction. Using the existing methods in literature, alarms of higher priorities could be easily overlooked in the pattern mining as they usually appear less frequent; thus, it makes the results become unreliable. Motivated by this issue, this paper proposes a new pattern mining method to extract priority-aware compact alarm patterns from historical alarm flood sequences. The main contributions of this study are twofold: (1) a priority-aware sequential pattern mining algorithm is designed to mine alarm sequential patterns using a closed sequential pattern mining algorithm with alarm priority information incorporated; (2) a pattern compression strategy is proposed to merge similar sequences into compact patterns, making it easier to search for alarm patterns. The effectiveness of the proposed pattern mining method is demonstrated by alarm data generated using a vinyl acetate monomer chemical plant model.

A framework via impulses-oriented Gini index and extreme value distribution for rolling bearing dynamic fault alarm and identification

Rolling bearing dynamic fault alarm and identification is essential in condition-based maintenance and can prevent serious accidents. However, an integrated framework with dynamic adaptability and full interpretability is rarely reported for this issue based on sparsity measures and statistical properties. Therefore, a framework via impulses-oriented Gini index and extreme value distribution is developed in this paper. First, for attenuating normal-phase amplitude oscillations of the Gini index, periodical impulses-oriented Gini index (PIGI) and sparsity-smoothed periodical impulses-oriented Gini index (SPIGI) are defined successively through enhanced envelope impulses extraction to improve incipient fault sensitivity and robustness against interferences. Next, a dynamic alarm threshold setting strategy is built by fitting the available PIGIs using generalized extreme value distribution (GEVD). Further, a novel framework, without prior knowledge and complex signal processing algorithms, is established based on PIGI, SPIGI, and GEVD-based thresholds for dynamic fault alarm and identification. The verification results on simulation and experimental data indicate that the proposed framework is effective in balancing false alarms and missed alarms and detecting incipient faults, and thus would have favorable application prospects.

Leak Detection in Natural Gas Pipelines Based on Unsupervised Reconstruction of Healthy Flow Data

Summary Timely detection of leak accidents plays an essential role in the safe operation and risk assessment of natural gas pipelines. However, the scarce leak data and complex operating conditions lead to small samples, data imbalance, and problems with confusing operating conditions. The reliance on leak data limits the recognition performance of the artificial intelligence classification method for leakage operating conditions. A leak detection method based on the unsupervised reconstruction of healthy flow data is established to address these problems. First, an unsupervised neural network is established to reconstruct healthy flow data from real natural gas pipelines. And a model update strategy based on active learning is designed to improve the model’s adaptability for time-varying pipelines. Next, a dynamic alarm threshold strategy that accounts for the knowledge of the experience and statistical characteristics of the data segments is suggested to prevent false alarms caused by ambiguous operating conditions. Finally, unlike most recent work that only considers simulated data or laboratory data, this paper conducts a leak case study on an actual natural gas pipeline in service to improve the robustness of the proposed method in the actual operating environment. The findings of this paper can be used as a reference to analyze pipeline behavior analysis based on pipeline flow trend characteristics and early alarm management.

A relative torsional vibration monitoring method for intelligent ships and associated error analysis

In this era of rapidly developing transport technology, shipping accounts for two thirds of the total international trade and transport industry. Nowadays, 'ship intelligence' is an important avenue to pursue in the development of the shipping industry and it requires ships and their working machinery to undergo real-time and high-precision inspection and diagnoses of their operational state. This paper develops a simple method for monitoring relative torsional vibration using multiple measuring points, which is suitable for the monitoring of intelligent ship rotating machinery. A proof-ofprinciple prototype is also designed to verify whether this calculational method can achieve clearer and more accurate physical significance and engineering practicability compared with the traditional single-point measuring method. According to the test results, under the condition of 100 pulses per revolution and at a speed of less than 3000 r/min, the relative error of speed measurement of the proof-of-principle prototype is within ±0.011%, which is close to the theoretical maximum accuracy of a 54 MHz sampling rate. The measurement result for the relative torsional angle shows that the maximum error is less than 0.17 mrad, the relative error is less than 1.75% and the maximum error in the amplitudes of the first and third harmonics is 9 mdeg. These accuracies are higher than those of the traditional method, which are 2.62% and 12 mdeg, respectively, in this study. Under the condition of speed fluctuations and rolling vibration, distortion occurs in the time-domain and frequency-domain torsional angle curves when the single-point measuring method is adopted. With the multi-point measuring method, much more accurate measurement values of the torsional angle are acquired compared with the single-point measuring method and accurate time-domain and frequency-domain curves are also obtained. Finally, the health status of the torsional angle is evaluated using an intelligent dynamic alarm system with a torsional vibration threshold, which has a better evaluation effect in line with the engineering practice and specification requirements. Therefore, this simple multi-point relative torsional vibration inspection method has universal applicability and wide application value.

Bearing mechanism and thickness optimization of ore roof in bauxite stope

Based on the elastic thin plate theory, the main law of the ore roof failure was analyzed and the formula of the ore roof thickness was deduced. The results show that the tensile stress in the roof center accounts for the roof failure. According to the limit failure conditions of the point, the formula of the ore roof thickness was derived. Taking No.10 stope of a bauxite mine as an engineering case, the optimal thickness of the ore roof was 0.36 m. The safety factor was taken as 1.3, therefore the design thickness was 0.5 m. In the whole industrial test process, the dynamic alarm devices did not start the alarm and the ore roof was not damaged. Compared with other stopes under similar conditions, its thickness was reduced by 0.1−0.3 m. The recovery rate of the ore roof was increased by 16.7%−37.5%.

Research and application of power grid intelligent inspection management system based on physical ID

Aiming at the problems of low efficiency, poor accuracy and untimely defect detection of traditional power grid equipment inspection methods, this paper proposed a power grid intelligent inspection management system based on physical ID. And a method for identifying high-risk alarm areas and fault location of transmission channel is proposed. By using big data mining and unsupervised clustering machine learning algorithm, the problems of poor accuracy and slow calculation speed of a large number of alarm data area division are fundamentally solved, and the functions of dynamic alarm and location affected by external force destruction, foreign object intrusion and environment are realized. The results show that compared with the traditional methods, the proposed method has higher efficiency and accuracy, and lower fault trip rate.

A dynamic alarm for passengers on travel

The motivation for every location based information system is: “To assist with the exact information, at right place in real time with personalized setup and location sensitiveness”. Now a days we live in an era of technology, smart phone become a part of our daily life. It is no longer a communication device only but also an essential part of our working life. In our project we have developed an android based application named “Android Location Alarm” at this application we tried to provide various location based services in one place. The app provides nearby service and it also has an alarm system that will work when user set the alarm to specific place and come near the place. The user may set, reset, disable, edit and set duration of the alarm as their wishes. User may view the destined locations on a map to check how far he/she is from the destined location.

Adaptive Early Warning Method Based on Similar Proportion and Probability Model

This paper presents a multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on a similar proportion and state probability model. Based on the similarity of historical equipment, the initial thresholds of different health states of equipment can be determined. The equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance. The obtained dynamic alarm lines at all levels can regulate the operation range of equipment in the different health states. Compared to the traditional method of a fixed threshold, this method can effectively reduce the number of false alarms and attains a higher prediction accuracy, which demonstrates its effectiveness and superiority. Finally, the method was verified by means of lifetime data of a rolling bearings. The results show that the model improves the timely detection of the abnormal state of the equipment, greatly reduces the false alarm rate, and even overcomes the limitation of independence between the fixed threshold method and equipment state. Moreover, multi-state division can accurately diagnose the current equipment state, which should be considered in maintenance decision-making.

A dynamic alarm threshold setting method for photovoltaic array and its application

Abstract The setting of the alarm threshold is critical for photovoltaic (PV) fault diagnosis systems. In practice the prior knowledge is generally used to set the alarm threshold with a fixed value. However, the output of a PV array varies with the external environment, and the distribution of the array electrical parameters has obvious volatility and nonlinearity. Therefore, it is difficult to judge the abnormal state of the PV system under all working conditions by setting the alarm threshold to a fixed value. To solve this problem, this paper proposes a new method for setting the alarm threshold of a PV array output. In this paper, the dynamic variation of the PV array output under different environmental conditions is analyzed, quantile method is adopted to set the threshold of the PV array output, and the dynamic threshold is used to realize the abnormal state judgment of the PV array. Finally, the effectiveness of the proposed method is demonstrated by experimental verification in the actual PV system. The proposed method is simple in calculation and can adapt to dynamic changes in the PV output, which provides a new method for the threshold setting of PV system fault diagnosis systems.

Alarm Monitoring for Multivariate Processes Based on a Convex-Hull Normal Operating Zone

Alarm monitoring, which is implemented by an alarm system, is to generate alarms for a process through certain mechanisms when the process is in the abnormal condition. This brief proposes a method to design an alarm system with dynamic alarm limits for multivariate processes. A normal operating zone (NOZ) described by a convex hull is established from the historical normal data points based on a formulated fitness index, and dynamic alarm limits of each process variable are developed based on the established NOZ. If all process variables are inside their own dynamic alarm limits, the process is in the normal condition and no alarms are raised; otherwise, the process is in the abnormal condition and alarms will occur. The designed alarm system can avoid false alarms from the conventional one using static alarm limits, owing to a fact that dynamic alarm limits of one process variable are related to other process variables. In addition, unlike designing alarm limits for latent variables with no specific meanings, each process variable has its own dynamic alarm limits that have the same physical meaning as the process variable. Industrial examples are provided to illustrate the effectiveness of the proposed method.

Designing Dynamic Alarm Limits and Adjusting Manipulated Variables for Multivariate Systems

Alarm systems are of paramount importance for safe and efficient operations of industrial plants. This paper proposes a method to design dynamic alarm limits and adjust manipulated variables for multivariate systems. A hyper-ellipsoid model is built from historical normal data points to represent a normal operating zone (NOZ). Dynamic alarm limits of each involved process variable are mathematically defined and calculated based on the NOZ. Adjustment amplitudes of some manipulated variables are determined in order to remove occurring alarms, by exploiting reduced NOZs in parallel coordinates. The effectiveness of the proposed method is illustrated via numerical and industrial examples.

A New Method for Alarm Monitoring of Equipment Start-Up Operations with Applications to Pumps

In practice, alarms configured for steady operating states often become a nuisance during the start-up operation of industrial equipment. As a result, they not only distract plant operators, but may also cause worse consequences, such as pump trips. Motivated by such as a practical problem, this paper presents a new alarm monitoring method for equipment start-up operations. The proposed method is capable of preventing nuisance alarms as well as achieving effective alarm monitoring during equipment start-ups. The contributions of this work are two-fold: First, an offline design framework is proposed to detect the maximum unsuppression delay time and formulate dynamic alarm limits; second, an online algorithm for alarm monitoring of equipment start-ups is proposed on the basis of the designed dynamic alarm limits and the calculation of an exact unsuppression delay time. The effectiveness and practicality of the proposed method are demonstrated by an industrial case study.

Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods

The presence of alarm floods is identified as the main reason for low efficiency of alarm systems and the leading cause of many industrial accidents. In practice, a commonly used technique to deal with alarm floods is dynamic alarm suppression, which temporally suppresses predefined groups of alarms following unplanned events that are not relevant or meaningful to the operator. However, determining what alarms to suppress from a pool of thousands of configured alarm variables remains a challenging problem. This paper proposes a data-driven method to find such alarm groups by detecting frequent patterns in alarm floods from historical alarm data. Main contributions of this study are: 1) the identification and extraction of alarm floods are formulated; 2) frequent alarm patterns are defined and itemset mining methods are adapted to discover meaningful patterns in alarm floods; and 3) new visualization techniques are proposed based on exiting plots to show alarm floods and alarm patterns. The effectiveness of the proposed method is demonstrated by application to real industrial data.

Dynamic Alarm Systems for Hospitals (D.A.S.H.)

Hospital alarms today indicate urgent clinical need, but they are seldom “true.” False alarms are contributing to the ever-increasing issue of alarm fatigue, or desensitization, among doctors and nurses. Alarm fatigue is a high-priority health care concern because of its potential to compromise health care quality and inflict harm on patients. To address this concern, we have engineered Dynamic Alarm Systems for Hospitals (D.A.S.H.), a dynamic alarm system that self-regulates alarm loudness based on the environmental noise level and incorporates differentiable and learnable alarms. D.A.S.H., with its ability to adapt to environmental noise and encode nuanced physiological information, may improve patient safety and attenuate clinician alarm fatigue.

A multi-setpoint delay-timer alarming strategy for industrial alarm monitoring

Alarm systems play an important role in industry to notify operators of abnormal and fault situations. In real industrial plants, however, a majority of abnormal alarm phenomena, including false alarms and missed alarms, always interfere with operators' judgment. Mathematical analysis of traditional univariate alarm techniques indicates that the alarm setpoint, the dynamic alarm order, and the alarm algorithm are the three main elements of alarm annunciation and alarm clearance. Based on this, we proposed a multi-setpoint delay-timer alarming strategy which optimizes the false alarm rate (FAR), the missed alarm rate (MAR), and the averaged alarm delay (AAD) by increasing the number of univariate alarm setpoints. The proposed alarming strategy is able to achieve a balance between alarming accuracy and alarming sensitivity. Its effectiveness is demonstrated with both simulation and industrial case studies.

ALACA: A platform for dynamic alarm collection and alert notification in network management systems

SummaryMobile network operators run Operations Support Systems that produce vast amounts of alarm events. These events can have different significance levels and domains and also can trigger other ones. Network operators face the challenge to identify the significance and root causes of these system problems in real time and to keep the number of remedial actions at an optimal level, so that customer satisfaction rates can be guaranteed at a reasonable cost. In this paper, we propose a scalable streaming alarm management system, referred to as Alarm Collector and Analyzer, that includes complex event processing and root cause analysis. We describe a rule mining and root cause analysis solution for alarm event correlation and analyses. The solution includes a dynamic index for matching active alarms, an algorithm for generating candidate alarm rules, a sliding window–based approach to save system resources, and a graph‐based solution to identify root causes. Alarm Collector and Analyzer is used in the network operation center of a major mobile telecom provider. It helps operators to enhance the design of their alarm management systems by allowing continuous analysis of data and event streams and predict network behavior with respect to potential failures by using the results of root cause analysis. We present experimental results that provide insights on performance of real‐time alarm data analytics systems.

Big data analytics for network and service management

Big data analytics for network and service management