A review on machinery diagnostics and prognostics implementing condition-based maintenance

There has been a growing interest in monitoring the ongoing "health" of products and systems in order to predict failures and provide warning to avoid catastrophic failure. Here, health is defined as the extent of degradation or deviation from an expected normal condition. While the application of health monitoring, also referred to as prognostics, is well established for assessment of mechanical systems, this is not the case for electronic systems. However, electronic systems are integral to the functionality of most systems today, and their reliability is often critical for system reliability. This paper presents the state-of-practice and the current state-of-research in the area of electronics prognostics and health management. Four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads. Examples are provided for these different approaches, and the implementation challenges are discussed.

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Prognostics and health management of electronics

Reliability is the ability of a product or system to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life-cycle environment. Commonly used electronics reliability prediction methods (e.g., Mil-HDBK-217, 217-PLUS, PRISM, Telcordia, FIDES) based on handbook methods have been shown to be misleading and provide erroneous life predictions. The use of stress and damage models permits a far superior accounting of the reliability and the physics of failure (PoF); however, sufficient knowledge of the actual operating and environmental application conditions of the product is still required.



This article presents a PoF-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life-cycle loading and failure mechanisms to perform reliability modeling, design, and assessment. This method permits the assessment of the reliability of a system under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition and the prediction of the future state of reliability. This article presents a formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty. Applications of PoF-based prognostics and health management are also discussed.


Keywords:

reliability;
prognostics;
physics of failure;
design-for-reliability;
reliability prediction

Prognostics and Health Management of Electronics

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

Data Mining in Manufacturing: A Review

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.

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Data mining in manufacturing: a review based on the kind of knowledge

A new approach to transparent embedding of data into digital images is proposed. It provides a high rate of the embedded data and is robust to common and some intentional distortions. The developed technique employs properties of the singular value decomposition (SVD) of a digital image. According to these properties each singular value (SV) specifies the luminance of the SVD image layer, whereas the respective pair of singular vectors specifies image geometry. Therefore slight variations of SVs cannot affect the visual perception of the cover image. The proposed approach is based on embedding a bit of data through slight modifications of SVs of a small block of the segmented covers. The approach is robust because it supposes to embed extra data into low bands of covers in a distributed way. The size of small blocks is used as an attribute to achieve a tradeoff between the embedded data rate and robustness. An advantage of the approach is that it is blind. Simulation has proved its robustness to JPEG up to 40%. The approach can be used both for hidden communication and watermarking.

SVD-Based Approach to Transparent Embedding Data into Digital Images

Adverse environmental conditions have combined cumulative effects leading to performance degradation and failures of avionics. Classical reliability addresses statistically-generic devices and is less suitable for the situations when failures are not traced to manufacturing but rather to unique operational conditions of particular hardware units. An approach aimed at the accurate assessment of the probability of failure of any avionics unit utilizing the known history-of-abuse from environmental and operational factors is presented herein. The suggested prognostic model utilizes information downloaded from dedicated monitoring systems of flight-critical hardware and stored in a database. Such a database can be established from the laboratory testing of hardware and supplemented with real operational data. This approach results in a novel knowledge discovery from data technology that can be efficiently used in a wide area of applications and provide a quantitative basis for the modern maintenance concept known as service-when-needed. An illustrative numerical example is provided.

Data mining technology for failure prognostic of avionics

The paper describes the design of a genetic classifier-based intrusion detection system, which can provide active detection and automated responses during intrusions. It is designed to be a sense and response system that can monitor various activities on the network (i.e. looks for changes such as malfunctions, faults, abnormalities, misuse, deviations, intrusions, etc.). In particular, it simultaneously monitors networked computer’s activities at different levels (such as user level, system level, process level and packet level) and use a genetic classifier system in order to determine a specific action in case of any security violation. The objective is to find correlation among the deviated values (from normal) of monitored parameters to determine the type of intrusion and to generate an action accordingly. We performed some experiments to evolve set of decision rules based on the significance of monitored parameters in Unix environment, and tested for validation.

Information Assurance in Computer Networks

A group testing method for finding patterns in data

The paper considers a multi-agent model of a computer networks security system, which is composed of particular autonomous knowledge-based agents, distributed over the hosts of the computer network to be protected and cooperating to make integrated consistent decisions. The paper is focused on an architecture, implementation and simulation of a case study aiming at exploration distinctions and potential advantages of using such an architecture for the computer network protection. The paper describes the conceptual model and architecture of the particular specialized agents and the system on a whole as well as implementation technology. Simulation scenario, input traffic model and peculiarities of the distributed security system operation are described. The major attention is paid to the intrusion detection task and agents interactions during detection of an attack against the computer network. The advantages of the proposed model of a computer networks security system are discussed.

Leonard J. Popyack

Papers

SVD-Based Approach to Transparent Embedding Data into Digital Images

Data mining technology for failure prognostic of avionics

Information Assurance in Computer Networks

A group testing method for finding patterns in data

Agent-Based Model of Computer Network Security System: A Case Study