Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Synchronised integrated online e-health profiles

Web-based social networking applications have become increasingly important in recent years. The current applications in the healthcare sphere can support the health management, but to date there is no patient-controlled integrator. This paper proposes a platform called Multiple Profile Manager (MPM) that enables a user to create and manage an integrated profile that can be shared across numerous social network sites. Moreover, it is able to facilitate the collection of personal healthcare data, which makes a contribution to the development of public health informatics. Here we want to illustrate how patients and physicians can be benefited from enabling the platform for online social network sites. The MPM simplifies the management of patients' profiles and allows health professionals to obtain a more complete picture of the patients' background so that they can provide better health care. To do so, we demonstrate a prototype of the platform and describe its protocol specification, which is an XMPP (Extensible Messaging and Presence Protocol) [1] extension, for sharing and synchronising profile data (vCard²) between different social networks.

Safety, courts and crime: occupational safety and health prosecutions in the Magistrates' courts

This paper reports on an empirically based study of occupational safety and health prosecutions in the Magistrates' courts in the State of Victoria, Australia. It examines the way in which the courts construct occupational safety and health issues during prosecutions against alleged offenders, and then theorises the role of the criminal law in health and safety regulation. The paper argues that courts, inspectors, prosecutors and defence counsel are involved in filtering or reshaping occupational safety and health issues during the prosecution process, both pre-trial and in court. An analysis of the pattern of investigation of health and safety offences shows that they are constructed by focusing on 'events', in most cases incidents resulting in injury or death. This 'event focus' ensures that the attention of the parties is drawn to the details of the incident and away from the broader context of the event. This broader context includes the way in which work is organised at the workplace and the quality of occupational safety and health management (the micro context), and the pressures within capitalist production systems for occupational safety and health to be subordinated to production imperatives (the macro context). In particular, during the court-based sentencing process, defence counsel is able to adopt a range of 'isolation' techniques that isolate the incident from its micro and macro contexts, thereby individualising and decontextualising the incident. The paper concludes that the legal system plays a key role in decontextualising and individualising health and safety issues, and that this process is part of the 'architecture' of the legal system, and a direct consequence of the 'form of law'.

Use of online health information to manage children’s health care: A prospective study investigating parental decisions

Background The use of the internet to access information is rapidly increasing; however, the quality of health information provided on various online sites is questionable. We aimed to examine the underlying factors that guide parents' decisions to use online information to manage their child's health care, a behaviour which has not yet been explored systematically. Methods Parents (N=391) completed a questionnaire assessing the standard theory of planned behaviour (TPB) measures of attitude, subjective norm, perceived behavioural control (PBC), and intention as well as the underlying TPB belief-based items (i.e., behavioural, normative, and control beliefs) in addition to a measure of perceived risk and demographic variables. Two months later, consenting parents completed a follow-up telephone questionnaire which assessed the decisions they had made regarding their use of online information to manage their child's health care during the previous 2 months. Results We found support for the TPB constructs of attitude, subjective norm, and PBC as well as the additional construct of perceived risk in predicting parents' intentions to use online information to manage their child's health care, with further support found for intentions, but not PBC, in predicting parents' behaviour. The results of the TPB belief-based analyses also revealed important information about the critical beliefs that guide parents' decisions to engage in this child health management behaviour. Conclusions This theory-based investigation to understand parents' motivations and online information-seeking behaviour is key to developing recommendations and policies to guide more appropriate help-seeking actions among parents.

Development of key soil health indicators for the Australian banana industry

To improve the sustainability and environmental accountability of the banana industry there is a need to develop a set of soil health indicators that integrate physical, chemical and biological soil properties. These indicators would allow banana growers, extension and research workers to improve soil health management practices. To determine changes in soil properties due to the cultivation of bananas, a paired site survey was conducted comparing soil properties under conventional banana systems to less intensively managed vegetation systems, such as pastures and forest. Measurements were made on physical, chemical and biological soil properties at seven locations in tropical and sub-tropical banana producing areas. Soil nematode community composition was used as a bioindicator of the biological properties of the soil. Soils under conventional banana production tended to have a greater soil bulk density, with less soil organic carbon (C) (both total C and labile C), greater exchangeable cations, higher extractable P, greater numbers of plant-parasitic nematodes and less nematode diversity, relative to less intensively managed plant systems. The organic banana production systems at two locations had greater labile C, relative to conventional banana systems, but there was no significant change in nematode community composition. There were significant interactions between physical, chemical and nematode community measurements in the soil, particularly with soil C measurements, confirming the need for a holistic set of indicators to aid soil management. There was no single indicator of soil health for the Australian banana industry, but a set of soil health indicators, which would allow the measurement of soil improvements should include: bulk density, soil C, pH, EC, total N, extractable P, ECEC and soil nematode community structure.

Do health inquiries lead to health system change? What have we learnt from recent health inquiries and will the same mistakes happen again?

Since 2001 there have been numerous Commissions of Inquiry into health system failures across the world. While the Inquiries were established to examine poor patient outcomes, each has identified a range of leadership and management shortcomings that have contributed to a poor standard of patient care. While there is an acknowledgement that different heath systems have different contexts, this paper highlights a number of themes that are common across Inquiries. It will discuss a number of common system failures in Inquiries spanning from 2001 to 2013 and pose questions as to why these types of failures are likely to re-occur, as well as possible learnings for health service management and leadership to address a number of these common themes.

Personal health data - Privacy policy harmonization and global enforcement

This workshop is jointly organized by EFMI Working Groups Security, Safety and Ethics and Personal Portable Devices in cooperation with IMIA Working Group "Security in Health Information Systems". In contemporary healthcare and personal health management the collection and use of personal health information takes place in different contexts and jurisdictions. Global use of health data is also expanding. The approach taken by different experts, health service providers, data subjects and secondary users in understanding privacy and the privacy expectations others may have is strongly context dependent. To make eHealth, global healthcare, mHealth and personal health management successful and to enable fair secondary use of personal health data, it is necessary to find a practical and functional balance between privacy expectations of stakeholder groups. The workshop will highlight these privacy concerns by presenting different cases and approaches. Workshop participants will analyse stakeholder privacy expectations that take place in different real-life contexts such as portable health devices and personal health records, and develop a mechanism to balance them in such a way that global protection of health data and its meaningful use is realized simultaneously. Based on the results of the workshop, initial requirements for a global healthcare information certification framework will be developed.

Lethal Parasites in Oysters from Coastal Georgia with Discussion of Disease and Management Implications

Extensive mortalities of oysters, Crassostrea virginica, occurred from 1985 through 1987 in coastal waters of Georgia. Fluid thioglycolate cultures of oysters collected from 16 of 17 locations revealed infections by the apicomplexan parasite Perkinsus marinus. An ascetosporan parasite, Haplosporidium nelsoni, was also observed in histopathological examination of oysters from 4 of the locations. While the range of H. nelsoni currently is recognized as the east coast of the United States from Maine to Florida, this is the first report of the parasite in Georgia waters. This paper documents the occurrence of these two lethal parasites in oysters from coastal waters of Georgia, along with potential disease and management implications. Results of an earlier independent and previously unpublished survey are also discussed which document the presence of P. marinus in Georgia as early as 1966.

Ciguatera Fish Poisoning: treatment, prevention and management

Ciguatera Fish Poisoning (CFP) is the most frequently reported seafood-toxin illness in the world, and it causes substantial physical and functional impact. It produces a myriad of gastrointestinal, neurologic and/or cardiovascular symptoms which last days to weeks, or even months. Although there are reports of symptom amelioration with some interventions (e.g. IV mannitol), the appropriate treatment for CFP remains unclear to many physicians. We review the literature on the treatments for CFP, including randomized controlled studies and anecdotal reports. The article is intended to clarify treatment options, and provide information about management and prevention of CFP, for emergency room physicians, poison control information providers, other health care providers, and patients.

The coastal environment and human health: microbial indicators, pathogens, sentinels and reservoirs

Innovative research relating oceans and human health is advancing our understanding of disease-causing organisms in coastal ecosystems. Novel techniques are elucidating the loading, transport and fate of pathogens in coastal ecosystems, and identifying sources of contamination. This research is facilitating improved risk assessments for seafood consumers and those who use the oceans for recreation. A number of challenges still remain and define future directions of research and public policy. Sample processing and molecular detection techniques need to be advanced to allow rapid and specific identification of microbes of public health concern from complex environmental samples. Water quality standards need to be updated to more accurately reflect health risks and to provide managers with improved tools for decision-making. Greater discrimination of virulent versus harmless microbes is needed to identify environmental reservoirs of pathogens and factors leading to human infections. Investigations must include examination of microbial community dynamics that may be important from a human health perspective. Further research is needed to evaluate the ecology of non-enteric water-transmitted diseases. Sentinels should also be established and monitored, providing early warning of dangers to ecosystem health. Taken together, this effort will provide more reliable information about public health risks associated with beaches and seafood consumption, and how human activities can affect their exposure to disease-causing organisms from the oceans.

Synopsis of researcher meeting -- Bottlenose Dolphin Health & Risk Assessment Project, February 22-24, 2005

A meeting was convened on February 22-24, 2005 in Charleston, South Carolina to bring together researchers collaborating on the Bottlenose Dolphin Health and Risk Assessment (HERA) Project to review and discuss preliminary health-related findings from captured dolphins during 2003 and 2004 in the Indian River Lagoon (IRL), FL and Charleston (CHS), SC. Over 30 researchers with diverse research expertise representing government, academic and marine institutions participated in the 2-1/2 day meeting. The Bottlenose Dolphin HERA Project is a comprehensive, integrated, multi-disciplinary research program designed to assess environmental and anthropogenic stressors, as well as the health and long-term viability of Atlantic bottlenose dolphins (Tursiops truncatus). Standardized and comprehensive protocols are being used to evaluate dolphin health in the coastal ecosystems in the IRL and CHS. The Bottlenose Dolphin Health and Risk Assessment (HERA) Project was initiated in 2003 by Dr. Patricia Fair at the National Oceanic and Atmospheric Administration/National Ocean Service/Center for Coastal Environmental Health and Biomolecular Research and Dr. Gregory Bossart at the Harbor Branch Oceanographic Institution under NMFS Scientific Research Permit No. 998-1678-00 issued to Dr. Bossart. Towards this end, this study focuses on developing tools and techniques to better identify health threats to these dolphins, and to develop links to possible environmental stressors. Thus, the primary objective of the Dolphin HERA Project is to measure the overall health and as well as the potential health hazards for dolphin populations in the two sites by performing screening-level risk assessments using standardized methods. The screening-level assessment involves capture, sampling and release activities during which physical examinations are performed on dolphins and a suite of nonlethal morphologic and clinicopathologic parameters, to be used to develop indices of dolphin health, are collected. Thus far, standardized health assessments have been performed on 155 dolphins during capture-release studies conducted in Years 2003 and 2004 at the two sites. A major collaboration has been established involving numerous individuals and institutions, which provide the project with a broad assessment capability toward accomplishing the goals and objectives of this project.

Health and disease status of Thai pangas, Pangasius hypophthalmus cultured in rural ponds of Mymensingh, Bangladesh

Thai pangas, Pangasius hypophthalmus is one of the important aquaculture species in Bangladesh. Over the last few years spectacular development has been taking place in Thai pangas farming in Mymensingh district. Due to availability of easy breeding and culture techniques as well as quick return, more and more people are converting their rice fields into pangas farms overnight. The present study was carried out to examine health and disease status of Thai pangas mainly through clinical, histopathological and bacteriological techniques. In addition, for collecting primary data on disease and health status of Thai pangas and the resultant socioeconomic impacts on rural households, questionnaire interview and participatory rural appraisal tools were used with selected farming households in three upazilas of Mymensingh district. The most prevalent diseases as reported by the farmers were red spot, followed by anal protrusion, tail and fin rot, pop eye, dropsy and gill rot. Other conditions like cotton wool type lesion, ulceration and white spot were reported but with lower incidence. Four isolates of Aeromonas hydrophila were recovered from kidney and lesion of diseased fish. Hemorrhage over the body especially near mouth and caudal region was noticed in the fishes associated with aeromonad infection. Internally, kidney, liver and spleen became swollen and enlarged. The isolates varied with their pathogenicity. All the four isolates were sensitive to Nitrofurantoin, Cotrimoxazole and Tetracycline but were resistant to Amoxycilline. An attempt was made to treat diseased fish with extracts from neem leaf, garlic and turmeric. Recovery of infection was monitored through mortality and histopathology. General histopathological changes of different organs were also studied. Extract from neem (Azadirachta indica) leaf gave better result. Telangiectasis, lamellar hypertrophy and hyperplasia hemorrhage, lamellar fusion, necrosis of lamellar epithelial cells, presence of parasites and their cysts were the major pathology of gills. Hemorrhagic lesion, pyknotic nuclei and melanomacrophage centers (MMC) were found in the liver of fish. Major pathologies in kidney of fish included presence of MMC, necrotic and ruptured kidney tubules, severe haemopoietic necrosis, and hemorrhage. The economic loss due to disease in Thai pangas farming was estimated from the difference between expected production and actual production. On an average, Thai pangas farmers of Mymensingh incur a loss of Tk. 23,104/ha/cycle due to fish disease (3.6% of expected total production). The loss, however, varied with location and size of farms, type of farmers and management practices. The study also highlighted fish health management related problems and recommended further work for the development of user-friendly farmer-oriented fish health management packages.