Technical Papers

Papers that have been authored by PHM Technology and its associates that have been presented at technical conferences.

The Digital Risk Twin – Enabling Model-based RAMS

The Digital Risk Twin – Enabling Model-based RAMS

While the definition of a Digital Twin (DT) is provided within recent literature, each DT should be designed to meet the specific requirements of the user. As RAMS is focused on the identification, understanding and mitigation of technical risk in a system, a RAMS engineer requires a DT that can autonomously establish the potential......
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How MADE Works

MADE: Development of an Aerospace PHM Software Tool

The Maintenance Aware Design environment (MADE) was conceived to provide a suite of software tools that could be used to design, assess and optimise Prognostics and Health Management systems for use in a wide variety of high risk industries where safety and reliability are critical, including mining, offshore and aerospace applications. MADE is currently being developed for application to aerospace systems and...
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The Language of FMEA

language of FMEA: effective use and reuse of FMEA data

Practical uses of Failure Modes and Effects Analysis (FMEA) range from the identification of potential design defects and safety hazards, to maintenance planning, diagnostics and Prognostics and Health Management (PHM). According to the broadly accepted standard for FMEA, MIL-STD-1629A, a successful FMEA is one that conducted in a timely manner, so that the results can be used to.....
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Automated design and optimisation of sensor set for CBM

Automated design - optimisation of sensor sets for CBM

A software tool was developed for automating the design, optimisation and performance assessment of sensor sets to support the Prognostics and Health Management (PHM) of the Joint Strike Fighter. The software (MADe) is based on a model-based simulation of failure propagation through the various subsystems of the aircraft to generate a system-level failure modes and effects database.....
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Applications of Artificial Intelligence and Decision-Making Methods in PHM

Applications of AI and Decision-Making Methods in PHM

Developing PHM capability for a system is a multi-staged process. This paper explores genetic algorithms, neural networks, fuzzy logic systems, AHP (Analytical Hierarchy Process), and Boolean logic to synthesize and fuse complex decisions arising in PHM design. Tools for PHM analysis are typically introduced and utilized towards the end of a products design or potentially after design. The methods proposed....
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Optimization of PHM System for Electronic Assemblies using MADE

Optimization of PHM Systems for Electronic Assemblies

Prognostics and health monitoring for electronic systems has been a field of interest of many researchers in the past decades. Traditionally, implementation of in-situ health monitoring for electronic systems has not been feasible due to time and cost considerations. However, recent research has led to improved sensing techniques and a better understanding of the manifestations and mechanisms.....
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The use of PHM or Autonomous Unmanned Air Systems

The use of PHM or Autonomous Unmanned Air Systems

Unmanned Air Systems (UAS) show great promise for a range of civilian applications, especially „dull, dirty or dangerous‟ missions such as air-sea rescue, coastal and border surveillance, fisheries protection and disaster relief. As the demand for autonomy increases, the importance of correctly identifying and responding to faults becomes more apparent, as fully autonomous systems must.....
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Sensor set optimization by functional model and Bayesian network for fault diagnosis

Sensor set optimization by functional model & Bayesian network for fault diagnosis

Turbine generator lubrication system, providing oil at an acceptable temperature, pressure, quantity and cleanliness to the bearings, is one of the safety-critical auxiliary systems in the power plant operation. The fault diagnosis capability of the system by utilizing relevant sensors is highly important but can be a great challenge since it comprises of numerous elements including tank.....
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Model Based Approach to Zonal Analysis

Model Based Approach to Zonal Analysis

Zonal Safety Analysis (ZSA) is an analysis technique for assessing the safety of complex systems however current tools limit its repeatability, thoroughness and time efficiency. The model based method proposed in this paper remedies these limitations. Zonal Safety Analysis is widely used in the aerospace industry with similar analysis techniques also seen in the design of offshore oil & gas systems.....
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Autonomous Prognostics and Health Management - APHM

Autonomous Prognostics and Health Management - APHM

The objective of this paper is to show how PHM con- cepts can be included in the design of an autonomous Un- manned Air Vehicle (UAV) and in doing so, provide effec- tive diagnostic/prognostic capabilities during system opera- tion. The authors propose a PHM Cycle that is divided into two parts, covering the design of the Autonomous PHM system and the operation of the PHM system in real-time application.....
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Influence of Environmental Loading Factors on System Design

Influence of Environmental Loading Factors on System Design

A closed-loop, iterative FMEA/FMECA process is vital for a safe design, but the challenge is in performing these analyses in such a way that alternate scenarios can be rapidly considered, and changes implemented to improve the design. This paper identifies an explicit model-based method to optimise system design by linking the failure identification process to specific operating scenarios.....
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Real-time Diagnosis of Physical Failures Using Causation-basd AI

Real-time Diagnosis of Physical Failures Using Causation-basd AI

Every system in existence is prone to failure and analysis and early detection of said failures (for Predictive Maintenance) is becoming a crucial aspect of modern systems design. Most catastrophic issues start from the smallest parts of a compo- nent within the system (physical failures) and minute changes to certain sensor readings from this level may indicate that an.....
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ML Detection and Isolation of Functional Failures Using Syndrome Diagnostics

ML Detection and Isolation of Functional Failures Using Syndrome Diagnostics

Failure identification in complex engineering systems often needs to be in form of a multilevel analysis, the first of which involves detection and isolation of functional disrup- tions across the system down to a discrete item. Considering that there is a functional flow(s) of operation in the machine, the loss or deviation of that functional flow(s) will imply a Functional.....
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Use of COTS Functional Analysis Software as an IVHM Design tool for Detection and Isolation of UAV Fuel System Faults

Use of COTS Functional Analysis Software as an IVHM Design tool for Detection and Isolation of UAV Fuel System Faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated......
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Towards Design of Prognostics Health Management Solutions for Maratime Assets

Towards Design of Prognostics Health Management Solutions for Maratime Assets

With increase in competition between OEMs of maritime assets and operators alike, the need to maximize the productivity of an equipment and increase operational efficiency and reliability is increasingly stringent and challenging. Also, with the adoption of availability contracts, maritime OEMs are becoming directly interested in understanding the.....
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Applications of Active Learning in Predictive Maintenance

Applications of Active Learning in Predictive Maintenance

Nowadays, the common choice in maintenance strategies is predictive maintenance (PdM), deprecating the corrective and preventive kinds. Even with various machine learning tech- niques to get advanced predictive models to achieve PdM, difficulties remain in the data acquisition process. While we have a plethora of unlabeled data from sensors, most of those available.....
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