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The purpose of this paper is to present the distribution of the magnetic field and additional losses analysis of the induction motors (IM) with opened and closed rotor slots.

In the field-circuit approach the distribution and changes of magnetic flux density in the motor are computed using a time-stepping finite element method. The additional losses in each element are evaluated at different frequencies.

An approximate analytical formulation is derived for rapid losses computation confirmed by the results of field-circuit method. For high-voltage motors due to the size ratios of the core and relatively deep stator and rotor slots major role in causing loss of higher harmonics play a fundamental slot harmonics. Higher harmonics order bigger than 100 cause only small part of total higher harmonics core losses. Closed rotor slots construction influenced significantly on no-load losses mainly due to reduction of losses at slot upper part. For nominal load condition that influence is not so strong according to the saturation of slot tips by rotor leakage flux. Nevertheless, core losses at load are several times higher as at no-load.

In future research authors will take into account motors feed from PWM inverter, working in the frequency range up to 400 Hz.

The results of investigation will be used in more detailed design of IMs especially for motors with closed rotor slots.

The methods presented in the paper was not used before. Also results of additional losses in the motor core calculation, especially according motors with closed slots at no load and load conditions are new.

The aim of this paper is to estimate the iron losses for an induction machine in the healthy case taking the slotting effect into account and to study the effect of an inter‐turn short‐circuit on these losses. Theoretical results are then compared with experimental ones.

A simple analytical model of iron losses allows one to calculate and to appreciate the contribution of the slotting effect on induction machine iron losses without and with an inter‐turn stator short‐circuit. This semi‐analytical approach is based on the iron stator and rotor flux density repartition which is deduced from the air‐gap flux density.

The iron losses are not only due to the fundamental air‐gap flux density, but also to the slotting harmonics. In fact, the slotting effect generates harmonic flux density waves with very low magnitudes but with high‐angular velocities, leading to non‐negligible harmonic iron dynamic losses which have similar values on both the stator and the rotor. The inter‐turn short‐circuit generates an iron losses and a slotting harmonic contribution increase.

Experimental measurements give the total iron losses. They do not allow separating the fundamental and the slotting harmonics contribution.

The knowledge of the iron losses behaviour in the healthy machine taking into account the slotting effect is important to optimize the design. The fault contribution on these losses allows one to estimate the damage which can be engendered by the fault.

Generally, iron losses studies and calculations are performed numerically using finite element software. The analytical approach can be interesting because it allows one to make faster calculations and to analyze the influence of the machine geometric parameters.

The electrical machines connected to modern electric power grids are non-sinusoidal excited, and their augmented losses, including iron losses, limit their working characteristics. This paper aims to propose a prediction method for iron losses in non-oriented grains (NO) FeSi sheets under non-sinusoidal voltage, involving an inverse classical Preisach hysteresis model and the time-integration of each loss component.

The magnetic history management in inverse Preisach model is optimized and a numerical Everett function is identified from measured symmetrical hysteresis cycles. The experimental data for sinusoidal waveforms obtained by a single sheet tester were also used to identify the parameters involved in Bertotti’ losses separation method. The non-sinusoidal magnetic induction waveform, corresponding to a measured voltage in an industrial electrical grid, was the input for Preisach model, the output magnetic field being accurately computed. The hysteresis, classical and excess losses are calculated by time-integration and the total losses are compared with those obtained for sinusoidal excitation.

The proposed method allows to estimate the iron losses for non-sinusoidal magnetic induction, using carefully identified parameters of FeSi NO sheets, using experimental data from sinusoidal regimes.

The method accuracy is assured by using a numerical Everett function, a variable Preisach grid step (adapted for the high non-linearity of FeSi sheets) and high-order fitting polynomials for the microscopic parameters involved in the excess loss estimation. The procedure allows a better design of magnetic cores and an improved estimation of the electric machine derating for non-sinusoidal voltages.

The purpose of this paper is to analyze the effect of various internal controls (i.e. hotlines, regular ethics (fraud) training, surprise audits, internal and external audits and background checks) on reducing occupational fraud losses by victim organizations.

The paper, based on data from an occupational fraud report co-authored by the Association of Certified Fraud Examiners (ACFE) and Peltier-Rivest (2007), uses a multivariate regression analysis to analyze the effect of various internal controls on preventing fraud losses.

The authors’ analyses demonstrate that hotlines, regular ethics (fraud) training, surprise audits and internal audits all decrease fraud losses when used separately. However, hotlines and surprise audits are the only statistically significant controls when controlling for the potential correlation among all internal controls. Hotlines are associated with a reduction of 54 per cent in median fraud losses, while surprise audits cut median losses by 69 per cent.

This study contributes to academia and the anti-fraud profession by assessing the statistical effect of six internal controls on preventing fraud losses, while controlling for the potential correlation among these controls.

This study discusses the relative benefits (loss savings) of various internal controls to organizations, governments, managers and anti-fraud professionals. This information may help determine investment priorities in the context of scarce resources.

This paper is based on proprietary data owned by the ACFE and is the first to analyze the statistical significance of various internal controls on the reduction of fraud losses in Canada.

The purpose of this paper is to identify the energy losses factors during the hydro-mechanical conversion process at high pressure via a novel reduced order dynamic model.

A novel reduced order dynamic model of the axial piston motor was proposed, which provides an explicit insight to the compression flow losses and the Coulomb friction losses. A fully coupled dynamic model of the piston motor was obtained based on the array bond graph method. And then, a reduced order model was obtained by the composition analysis of flow and torque of the axial piston motor. After that, the energy losses estimation model was presented to predict the energy loss of the piston motor under a wide range of working conditions. The model was verified by comparing the experimental and simulation results.

The simulation result indicates that the flow loss caused by oil compression accounts for 59 per cent of the total flow loss, and the Coulomb friction torque accounts for 40 per cent of the total torque loss under a specific working condition. The compression flow loss and Coulomb friction torque are the major factors that lead to the aggravation of energy loss under extreme working conditions of the piston motor.

At high-pressure condition, the compression flow losses due to fluid compressibility cannot be neglected, and the hydro-mechanical losses in varies friction pairs should involve Coulomb friction losses. Flow and torque loss analytical expression in the model involve the design and control parameters of the piston equipment, which can realize the parameter optimization of the piston equipment for the purpose of energy-saving.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

This study aims to contribute to the debate on goodwill accounting by examining the information content of impairment losses recognized in half-yearly reports. Half-yearly reports provide a suitable context to examine the effectiveness of the impairment process. Due to IFRIC 10 requirements, indeed, managers may have incentives to avoid recognizing impairment losses at the interim reporting date.

The study adopts an archival approach. Based on the traditional Ohlson’s model (1995), it explores the information content of half-yearly impairment losses in the European context over the period 2007–2017.

Findings confirm the relevance of half-yearly reports and suggest that half-yearly impairment losses are significantly associated with stock prices. In particular, investors positively value companies that recognized goodwill impairment losses at the interim reporting date.

The study contributes to the academic debate on goodwill and the effectiveness of the impairment procedure. In particular, it provides empirical evidence on the recognition of goodwill write-offs when it is possible to avoid the impairment test in the absence of indications of impairment.

Findings of this study can support the current debate on accounting for goodwill also in the light of the recent proposals of the IASB on the need to improve the effectiveness of the impairment test.

This study provides original empirical evidence on the goodwill impairment test in half-yearly reports, extending previous research that typically examines this issue in annual reports.

Previous research in auditing has used the probability of small profits or losses as a measure of audit quality. The purpose of this paper is to investigate the validity of the underlying assumption in prior audit literature that auditing mitigates clients’ inclination towards loss avoidance and to shed light on the debate regarding earnings discontinuity.

This paper compares the discontinuity in earnings distribution around zero, both before and after auditing.

Using a unique data set that contains both recorded and waived adjustments, the authors find that audit adjustments do not reduce the discontinuity in earnings distribution around zero.

The results advise caution in using the probability of small profits or losses as a measure of audit quality. The findings suggest the discontinuity in earnings around zero may not be caused by loss avoidance achieved through accounting misreporting, which falls under the purview of auditing.

This research makes unique contributions beyond those of prior studies. By incorporating waived adjustments, the authors are able to conduct more comprehensive tests and explore richer details of audit adjustments that were not available in previous studies. The proportion of losses in this study's sample aligns with that in prior US research, which enhances the generalisability of the authors’ findings and minimizes the influence of inherent discrepancies in auditors' motivations to curb loss avoidance.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Small firms, which represent much of the Silicon Valley region, tend to experience losses due to their small scale, small customer base and lack of diversification. The authors study the impact of accounting conservatism and losses on firm value and as such this study is an appropriate addition to this growing field of financial management.

The authors use methodology developed in prior literature to examine Silicon Valley and non-Silicon Valley firms' and their behavior when facing losses and the factors, which might play a role in their valuation. The authors focus particularly on earnings and accounting conservatism. Accounting conservatism captures how fast firms record losses relative to gains. The faster losses are recognized than gains the more accounting conservatism is exhibited. The authors examine the seemingly unrelated estimation of differences in means for our independent variables of interest across the two samples of Silicon Valley and non-Silicon Valley firms, both earnings and accounting conservatism. The authors use matched sample analysis of these firms based on four digit SIC code, size and date. In robustness, the authors run a more in-depth propensity score matched sample analysis.

The authors document that market values of Silicon Valley firms with accounting losses are affected less by negative earnings than other firms with accounting losses in the United States outside of the Silicon Valley region, noting the “lose big, win bigger” sentiment of Silicon Valley. Additionally, the authors document that accounting conservatism does play a role in influencing valuations of companies with accounting losses both in Silicon Valley and the rest of the United States, marginally more for Silicon Valley firms.

This study would be of interest to fund managers who need to consider smaller firms for inclusion in their portfolios. A lot of small firms have experienced losses ever since going public, especially Silicon Valley start-up firms.