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Managing asset integrity is crucial for the cost-effective asset management of processing plants. Consequently, new techniques for detection and classification of incipient faults are continuously being sought by industry and developed by research and development professionals, both corporate and academic. Although acoustic emission (AE) testing for faults in static equipment has been used since the 1970s, its use as a monitoring technology for various machinery conditions has been poorly adopted by industry, despite a significant volume of work having been published over the past twenty years describing success in detecting numerous rotating and reciprocating machinery faults. Anecdotal evidence from industry suggests that many 'tried and failed'. The authors believe that this is because applying AE monitoring to industrial plant is fraught with poorly documented challenges, obstacles, and limitations that must be well understood and overcome before any reported results can be replicated. Thus, to enable potential users to approach AE monitoring with more realistic expectations, the current paper discusses several such problems and suggests some available techniques for their management. After an initial introduction on the basics of AE specifically applied to machinery monitoring, issues are divided into generic problems and application-specific challenges.

The relative risk of total knee arthroplasty (TKA), high tibial osteotomy (HTO), and medial unicompartment (UKA) replacement for medial compartment arthritis is presented. Risk is defined as the product of the probability of an event occurring and its consequence. To define consequence, 2 related scales of impact (1 systemic and 1 local) are suggested. The probability of a complication is derived from the incidence as found in the published literature and expressed as a decimal of 1. The cumulative risk is expressed as the sum of the risks of all individual complications. The overall impact of specific comorbidities has been calculated when their influence on the incidence of a particular complication is known. Of the 3 operations, TKA has the highest cumulative risk of systemic complications and HTO is the most likely to produce local technical problems. UKA is the safest of the procedures. The relative risk of TKA:HTO:UKA is 1.00:1.01:0.31. For TKA, the greatest additional risk is morbid obesity, which increases overall risk by 31% by virtue of a 7.8-fold increase in infection rate. Cardiorespiratory disease, diabetes mellitus, smoking, and cirrhosis of the liver increase cumulative risk by 20%, 17%, and 17%, respectively.The authors conclude that a quantitative assessment of operative risk is possible and useful. However, it depends on the availability of reliable complication incidence data.

Conveyor belt wear is an important consideration in the bulk materials handling industry. We define four belt wear rate metrics and develop a model to predict wear rates of new conveyor configurations using an industry dataset that includes ultrasonic thickness measurements, conveyor attributes, and conveyor throughput. All variables are expected to contribute in some way to explaining wear rate and are included in modeling. One specific metric, the maximum throughput-based wear rate, is selected as the prediction target, and cross-validation is used to evaluate the out-of-sample performance of random forest and linear regression algorithms. The random forest approach achieves a lower error of 0.152 mm/megatons (standard deviation [SD] = 0.0648). Permutation importance and partial dependence plots are computed to provide insights into the relationship between conveyor parameters and wear rate. This work demonstrates how belt wear rate can be quantified from imprecise thickness testing methods and provides a transparent modeling framework applicable to other supervised learning problems in risk and reliability.

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For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an individual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.