Developing the Job Hazard Analysis

Crutchfield, Nathan; Roughton, James E.

doi:10.1016/b978-0-12-396496-0.00012-4

Cited by 4 publications

(5 citation statements)

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“…Some obstacles resisted the relocation process during the implementation, especially in the second stage (Santoso and Anggraini 2021). However, based on the aspects of unsuccessful relocation according to Job Hazard Analysis (JHA) (Crutchfield and Roughton 2014), namely inadequacy of new sites, distance from livelihoods and social networks, socio-culturally inappropriate settlement layouts, lack of community participation, and underbudgeting of relocation costs, these aspects are not relevant to the context of the relocation of residents of the Ria-Rio Reservoir.…”

Section: Factors Supporting the Implementation Of Government Policymentioning

confidence: 99%

Public Policy Implementation of the Jakarta Government's Policy: Study of Community Relocation Around the Reservoir

Satispi

Samudra

2021

the.soc.pers.j.

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This study aims to understand the Jakarta government's policy in relocating people from the Ria Rio Reservoir and placing them in flats (rusunawa). Based on the research findings that have been done, it is concluded that strict facility restrictions are necessary to prevent the transfer of rental rights from low-income people to the upper class. The existence of a Management Agency is deemed necessary by residents. It is evidenced by the high number of answers residents require of a Management Agency and supported again by the feeling of obedience to the residents' management board. Constraints in the provision of housing on the need side It is difficult to invite low-income people and residents of slums who are generally classified as poor to want to live in apartment units. Performance measurement cannot be done only on a one-year deadline. Performance has been disrupted by past policies that have prevented the achievement of housing development targets (rusunawa) from being fully met. A synergy between regional and central interests is needed. Program handling must be in synergy with other program policies.

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Section: Factors Supporting the Implementation Of Government Policymentioning

confidence: 99%

Public Policy Implementation of the Jakarta Government's Policy: Study of Community Relocation Around the Reservoir

Satispi

Samudra

2021

the.soc.pers.j.

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“…The use of JSA requires resources in terms of time (Roughton and Crutchfield, 2008). It may thus cause resistance, and arguments such as "our employees know their job", the result being that the JSA is seldom used (Crutchfield and Roughton, 2014). Use of JSA can also meet resistance among managers and employees due to a belief that it is unnecessary to employ the method (Yoon et al, 2011).…”

Section: Literature Review: Merits and Limitations Of Jsamentioning

confidence: 99%

The application and benefits of job safety analysis

2019

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“…1) Identification of critical components: one way to identify critical components in a complex system is by using hazard analysis [6]. Hazard analysis is a methodology to estimate the likelihood that a condition or event might happen, which could lead to an undesirable circumstance [7].…”

Section: A Towards Data-driven Prognosticsmentioning

confidence: 99%

Component based data-driven prognostics for complex systems: Methodology and applications

Mosallam¹,

Medjaher²,

Zerhouni³

2015

2015 First International Conference on Reliability Systems Engineering (ICRSE)

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In recent years, considerable research efforts have been applied in the field of fault prognostics. However, to the authors knowledge, there are few published works that address complete and systematic methods describing the steps required to develop data-driven prognostics approaches for complex systems. This paper presents a generic component-based prognostics methodology that can be customized for different applications and which can be useful for new researchers and engineers. The paper is divided into two parts. The first part provides a description of the procedures required before constructing data-driven prognostics, such as identifying critical components, selecting physical parameters to monitor, choosing monitoring sensors and defining the data acquisition system. The second part presents a novel data-driven prognostic method for direct remaining useful life (RUL) prediction. This method relies on two phases: offline and online. In the offline phase, a method for constructing health indicators (HI) from sensor data is presented. Such HIs can be used as offline models to display the deterioration evolution of components over time. In the online phase, similar HIs are constructed from the sensor data for a new component. Then, a discrete Bayesian filter is applied to estimate the current health status. Finally, the offline database is searched to find the closest group to the online HIs. The selected offline HIs can be used for estimating the RUL of the new component under operation. The performance of the method is demonstrated using two real data sets taken from the NASA Ames prognostics data repository. Index Terms-Data-driven prognostics, remaining useful life, health indicators construction, discrete Bayes filter, Gaussian process regression.

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Developing the Job Hazard Analysis

Cited by 4 publications

References 0 publications

Public Policy Implementation of the Jakarta Government's Policy: Study of Community Relocation Around the Reservoir

Public Policy Implementation of the Jakarta Government's Policy: Study of Community Relocation Around the Reservoir

The application and benefits of job safety analysis

Component based data-driven prognostics for complex systems: Methodology and applications

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