Inductive Monitoring System Research Articles

Diagnostic Methodology of the Low Displacement Diesel Engine Vibration Signal for Identification of Operating Conditions

The power developed by an engine is one of the main indications of its operating condition since it directly reflects changes in its performance and, therefore, the possibility of faults or anomalies. Usually, this parameter is measured directly by the use of dynamometers. However, in most real operating conditions, it is impractical or impossible to install this equipment. Additionally, diagnostic techniques generally require a level of intrusion that can cause engine damage. Therefore, in this study, a diagnostic methodology based on the indirect measurement of the engine output power is presented. The experimental procedure has been carried out in a single-cylinder diesel engine, which operates in four different operating conditions: 25%, 50%, 75%, and 100%. The methodology is based on the use of the engine vibration signal as a tool to identify different operating loads. By calculating the velocity of the vibration and the implementation of an algorithm based on the Inductive Monitoring System (IMS), it has been possible to relate the changes in the speed of vibration to the percentage of engine load. This methodology has showed an accuracy between a range of 84% and 94%, which demonstrates its reliability.

Identification of power output of diesel engine by analysis of the vibration signal

Power output is one of the main working parameters of a diesel engine, which not only reflects its dynamic performance but also acts as an important indicator of its operating state. At present, diesel engine power monitoring mainly depends on dynamometers, but most engines cannot be so equipped during operation. In addition, other techniques, such as in-cylinder pressure measurements used to gauge power output are invasive and have higher requirements for sealing and reliability. Therefore, it is important to study other means for achieving real-time monitoring of engine power that do not damage the engine. In this paper, we use the vibration acceleration of the combustion phase in the angle domain as the original signal to identify the output power. However, the signal contains many interference signals and must be pre-processed by purification techniques such as filtering, de-noising and empirical mode decomposition. Finally, the automatic identification of the power of a diesel engine is realized by means of inductive monitoring system algorithms. The test results show that the recognition accuracy under the five different working loads ranges between 83% and 95%, which demonstrates the effectiveness of the proposed method.

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments.

Application of Inductive Monitoring System to Plug Load Anomaly Detection

NASA Ames Research Center’s Sustainability Base is a new 50,000 sq. ft. LEED Platinum office building. Plug loads are expected to account for a significant portion of the overall energy consumption. This is because building design choices have resulted in greatly reduced energy demand from Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are major contributors to energy consumption in traditional buildings. In anticipation of the importance of plug loads in Sustainability Base, a pilot study was conducted to collect data from a variety of plug loads. A number of cases of anomalous or unhealthy behavior were observed including schedule-based rule failures, time-to-standby errors, changed loads, and inter-channel anomalies. These issues prevent effective plug load management; therefore, they are important to promptly identify and correct. The Inductive Monitoring System (IMS) data mining algorithm was chosen to identify errors. This paper details how an automated data analysis program was created, tested and implemented using IMS. This program will be applied to Sustainability Base to maintain effective plug load management system performance, identify malfunctioning equipment, and reduce building energy consumption.

Space shuttle main propulsion system anomaly detection: A case study

The space shuttle main engine (SSME) is part of the Main Propulsion System (MPS) which is an extremely complex system containing several sub-systems and components, each of which must work precisely in order to achieve a successful mission. A critical component under study is the flow control valve (FCY) which controls the pressure of the gaseous hydrogen between the SSME and the external fuel tank. The FCV has received added attention since a Space Shuttle Mission in November 2008, where it was discovered during the mission that an anomaly had occurred in one of the three FCV's. Subsequent inspection revealed that one FCV cracked during ascent. This type of fault is of high criticality because it can lead to potentially catastrophic gaseous hydrogen leakage. A supervised learning method known as Virtual Sensors (VS), and an unsupervised learning method known as the Inductive Monitoring System (IMS) were used to detect anomalies related to the FCV in the MPS. Both algorithms identify the time of the anomaly in a multi-dimensional time series of temperatures, pressures, and control signals related to the FCV. This discovery corroborates the results of the inspection and also reveals the time at which the anomaly likely occurred. The methods were applied to data obtained from the March 2009 launch of Space Shuttle Discovery to determine whether an anomaly occurred in the same sub-system. According to our models, the FCV sub-system showed nominal behavior during ascent.

Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype


 
 
 In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the In- ductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investi- gate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were pre- viously unknown and not previously simulated will be studied in earnest, as well as apparent de- ficiencies or misapplications that result from us- ing the data-driven paradigm. Our conclusions indicate that robust detection performance of sim- ulated failures using IMS is not appreciably af- fected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.
 
 

Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring

This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds.The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms.The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.

Application of Inductive Monitoring System for equipment condition monitoring

Abstract In most industries model based reasoning techniques are used as the monitoring and diagnosis scheme for process faults. But even though model-based reasoning is an excellent tool for performing system monitoring, it is often difficult and time consuming to build the right model for the system. Inductive Monitoring System (IMS) is a technique that automatically produces health monitoring knowledge bases for systems that are either difficult to model or which require models that are too complex to use for real time monitoring. The technique automatically defines groups of consistent system parameter data by examining nominal system data and generates a normal operation baseline. It uses a clustering algorithm to form groups of nominal values for sets of related parameters. The nominal values generate constraints on those parameter values that should hold during nominal operation. During abnormal operation, the constraints set on the parameter values are broken and IMS automatically identifies the anomalous behavior of the system. IMS can also provide a statistically weighted measure of the deviation of current system behavior from the normal operation baseline. The advantage of using IMS is that it is fast and simple yet very effective. Each of the clusters generated by IMS represents a different mode of operation of the plant. Abnormal behavior can also be grouped into clusters and identified as specific failure mode. This paper explains the application of IMS for monitoring industrial processes with specific case studies concerned with monitoring of rotating equipment.

A new measuring sensor for pump control in subsea separators

A novel sensor developed by ABB for measuring the water/oil interface level in subsea gravity separators used in offshore oil and gas production allows better control of the separators' water outlet pumps. Designated ILMS, for Inductive Level Monitoring System, it represents a key technology for subsea processing. In addition, it can provide information on the quality of the separation process, enabling the amount of separation-enhancing chemical additives to be reduced. Operating costs and environmental impact are lower as a result.

Precision inductive position monitoring system for use in cryogenic environments

In cryogenic systems it is sometimes necessary to achieve accurate remote position sensing. An inductive position readout system requiring only small LC resonators as the sensor elements is described.