False Negative Indications Research Articles

Induction Motor Speed Estimation Based on Airgap Flux Measurement Using Hilbert Transform and Fast Fourier Transform

This paper proposes the rotor speed estimation of a squirrel cage induction motor fed by a sinusoidal power supply or an inverter based on the use of a Hall effect sensor installed between two stator slots of the machine. As well known in literature, inverter-fed induction motors usually contains a series of current harmonics and in case of rotor faults, such as broken bars, this operational condition becomes even more severe. The speed estimation has been extensively investigated for its use in control schemes, to compute output power or machine efficiency and particularly for fault diagnosis using motor current signature analysis. In this method, it is necessary an accurate speed estimation to prevent false positives and false negative indications. Therefore, the present research has been carried out by applying the Fast Fourier Transform and Hilbert Transform in the airgap signal measured by the Hall sensor, in order to estimate the rotational speed of the induction motor in a healthy condition and with broken rotor bars. This approach has been compared to the same signal processing techniques using the stator current phase, usually used for speed estimation in the state of the art solutions. The efficiency of this approach was evaluated from simulation and experimental tests, for motor running at distinct load torques, at very low slip conditions and considering a damaged rotor cage. The simulation and experimental results have shown a better accuracy with the measured or simulated speeds, using the airgap estimations when compared to those obtained from stator current processing.

Reliable Airgap Search Coil Based Detection of Induction Motor Rotor Faults Under False Negative Motor Current Signature Analysis Indications

Rotor cage damage in induction motors must be detected and repaired to prevent costly in-service failures that can occur due to broken cage fragments or arcing. Motor current signature analysis (MCSA) has become the most widespread test in the field for detecting these faults as it can provide remote, online monitoring using current sensors available in the motor control center. Although MCSA has been successful in detecting broken rotor bars, many cases of false indications have been reported in the field. False negative indications, where MCSA fails to detect the fault due to load variation, low slip operation, error in speed estimate, and nonadjacent broken bars, is considered serious as it can lead to unexpected motor failure. This has triggered research on alternative monitoring techniques for reliable detection of rotor faults with flux monitoring being one of them, as it can be implemented with low cost search coils. In this article, a new test method based on time–frequency analysis of the airgap flux is proposed and verified. It is shown that the second rotor rotational frequency (2×) upper sideband can provide reliable detection of rotor faults. Experimental test results are given under load variation, low slip, and nonadjacent broken bar conditions to support the claims on the reliability of the proposed method for cases where MCSA produces false negative indications.

Abstract P505: Real-world Data Validation Of A Novel P-wave Based Automatic Atrial Fibrillation Detection Algorithm

Background: Automatic ECG algorithms using only RR-variability in ECG to detect AF have shown high false positive rates. By including P-wave presence in the algorithm, research has shown that it can increase detection accuracy for AF. Methods: A novel RR- and P-wave based automatic detection algorithm implemented in the Coala Heart Monitor ("Coala", Coala Life AB, Sweden) was evaluated for detection accuracy by the comparison to blinded manual ECG interpretation based on real-world data. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where the algorithm had detected both irregular RR-rhythms and strong P-waves in either chest or thumb recording (non-AF episodes classified by algorithm as Category 12).The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown.The blinded recordings were each manually interpreted by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the detection algorithm to determine the number of additional false negative indications for AF as presented to the user. Results: The trained cardiologist manually interpreted 0 of the 100 recordings as AF. Manual interpretation showed that the novel automatic AF algorithm yielded 0 % False Negative error and 100 % Negative Predictive Value (NPV) for detection of AF. Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings). The 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves constituted 18% of all recordings with irregular RR-rhythms. Respiratory sinus arrhythmia was the single most prevalent condition and was found in 47% of irregular RR-rhythms with strong P-waves. Conclusion: The novel, P-wave based automatic ECG algorithm used in the Coala, showed a zero percent False Negative error rate for AF detection in ECG recordings with RR-variability but presence of P-waves, as compared to manual interpretation by a cardiologist.

A Histogram of Oriented Gradients Approach for Detecting Broken Bars in Squirrel-Cage Induction Motors

This article presents a novel approach for detecting broken rotor bars in squirrel-cage induction motors, using a time-domain current analysis. More particularly, this solution proposes a new use of histogram of oriented gradients since this method is usually applied in computer vision and image processing applications. Fully broken rotor bars have been detected when the motor was running at a very low slip since this operational condition is very difficult to identify using the traditional motor-current signature analysis. In addition, only one phase of the stator current of the machine was applied to extract the intensity gradients and edge directions of each current time window, for both healthy and damaged rotors. It is important to highlight that the present method does not require the slip measurement for fault detection, as demand for other techniques and often related to false negative indications. The features extracted from the histograms have been applied as inputs for a neural network classifier. This method has been validated using some experimental data from a 7.5-kW squirrel-cage induction machine running at distinct load levels (slip conditions) and also for oscillatory loads.

Limits of special material detectability fundamental to idealized dual-energy radiographic systems

Abstract Analysis of dual-energy radiographic approaches to the detection of “special” material, i.e. contraband, in otherwise innocuous scenes, such as maritime cargo containers encountered at ports of entry, reveals fundamental limitations of data obtained along a single line-of-sight. The basic equations that describe idealized systems are presented and analyzed so as to identify challenges that are only exacerbated by scatter, noise, and measurement uncertainty. In the mono-energetic limit, the instrumentation and measurement techniques reduce to a system of linear equations that are simple to treat: it is shown that there exists a threshold for the minimum amount of high-Z material that can be detected unambiguously (without the risk of false positive or false negative indications), even when the background materials are of finite and of well-known variety. The threshold depends on the quality of prior information about the cargo and the selection of probing energies, and can therefore be minimized by good system design practices and measurement procedures, but persists as a general challenge to security operations. In discussion of this result, it is noted that techniques incorporating other prior information, such as the geometry of cargo and potential contraband, along with data from 2D imaging, have far greater potential. As an example, a simple data analysis method capable of differentiating high-Z spheres from those of more common material is presented. This simple example demonstrates that, although simple line-of-sight transmission radiography may suffer fundamental limitations, there remains good cause to be optimistic about the potential for image analysis in radiographic material identification.

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false negative indications are more frequently. The efficiency of this approach has been proven using some computational simulation results and experimental tests to detect fully broken rotor bars in a 7.5 kW squirrel cage induction machine fed by line and an inverter.

Iterative two-stage approach for identifying structural damage by combining the modal strain energy decomposition method with the multiobjective particle swarm optimization algorithm

An iterative two-stage structural damage identification approach that combines the modal strain energy decomposition (MSED) method with the multiobjective particle swarm optimization algorithm is presented. The proposed scheme is inspired by the general noniterative two-stage approach, which attempts to locate damage in the first stage and estimate the severity of damage in the second stage; however, the present approach differs by constructing an iterative MSED indicator to perform damage localization and severity estimation in a cyclic manner. Consequently, false positive indications of damage are gradually suppressed, and the precision of the damage severity estimation is improved as the iteration continues. In addition, a new element activation operation is embedded into the iterative process; in this step, possible false negative indications of damage excluded in the first stage can be activated and subsequently retested to identify damage, thereby reducing the false negative rate. Furthermore, a comparative study is conducted between the iterative two-stage approach and the general noniterative approach by contemplating a 3-D structure, and the effects of measurement noise and spatial incompleteness are considered.

Asymmetric Effects of False Positive and False Negative Indications on the Verification of Alerts in Different Risk Conditions

Indications from alerts or alarm systems can be the trigger for decisions, or they can elicit further information search. We report an experiment on the tendency to collect additional information after receiving system indications. We varied the proclivity of the alarm system towards false positive or false negative indications and the perceived risk of the situation. Results showed that false alarm-prone systems led to more frequent re-checking following both alarms and non-alarms in the high risk condition, whereas miss-prone systems led to high re-checking rates only for non-alarms, representing an asymmetry effect. Increasing the risk led to more re-checks with all alarm systems, but it had a stronger impact in the false alarm-prone condition. Results regarding the relation of risk and the asymmetry effect of false negative and false positive indications are discussed.

Predicting Binding Regions within Disordered Proteins.

Disordered regions are sequences within proteins that fail to fold into a fixed tertiary structure and have been shown to be involved in a variety of biological functions. We recently applied neural network predictors of disorder developed from X-ray data to several protein sequences characterized as disordered by NMR (Garner, Cannon, Romero, Obradovic and Dunker, Genome Informatics, 9:201-213, 1998). A few predictions on the NMR-characterized disordered regions were noted to contain false negative indications of order that correlated with regions of function. These and additional examples are examined in more detail here. Overall, 8 of 9 functional segments in 5 disordered proteins were identified or partially identified by this approach. The functions of these regions appear to involve binding to DNA, RNA, and proteins. These regions are known to undergo disorder-to-order transitions upon binding. This apparent ability of the predictors to identify functional regions in disordered proteins could be due to the existence of different flavors, or sub-classes of disorder, originating from the sequence of the disordered regions and perhaps owing to local inclinations toward order. These different flavors may be a characteristic that could be used to identify binding regions within proteins that are difficult to characterize structurally.

PROBLEMS FOUND USING A RADON STRIPPING ALGORITHM FOR RETROSPECTIVE ASSESSMENT OF AIR FILTER SAMPLES

An evaluation of a large number of air sample filters was undertaken using a commercial alpha and beta spectroscopy system employing a passive implanted planar silicon (PIPS) detector. Samples were only measured after air flow through the filters had ceased. Use of a commercial radon stripping algorithm was implemented to discriminate anthropogenic alpha activity on the filters from the radon progeny. When uncontaminated air filters were evaluated, the results showed that there was a time-dependent bias in both average estimates and measurement dispersion of anthropogenic activity estimates with the relative bias being small compared to the dispersion, indicating that the system would not give false positive indications for an appropriately set decision level. By also measuring environmental air sample filters simultaneously with electroplated alpha sources, use of the radon stripping algorithm demonstrated a number of substantial unexpected deviations from calibrated values indicating that the system would give false negative indications. Use of the current algorithm is, therefore, not recommended for general assay applications. Use of the PIPS detector should only be utilized for gross counting without appropriate modifications to the curve-fitting algorithm. As a screening method, the radon stripping algorithm might be expected to see elevated alpha activities from pure Pu on air sample filters (not due to radon progeny) around the 200 disintegrations per minute level.

Information-seeking behaviour of sniffer dogs during match-to-sample training in the scent lineup

Qualitative and quantitative changes in dogs’ information-seeking behaviours during the subsequent phases of operant conditioning training using a scent lineup, were investigated. Particular interest was paid to behaviours which may have an impact on errors committed by dogs at work in a scent lineup and thus on the reliability of the canine identification of humans on the base of scent. Significant individual differences were found in dogs’ performance in operant conditioning during match-to-sample trials. The style and time of sniffing the pattern scent sample as well as the searching time and number of stations sniffed in the lineup were found to influence the percentage of errors (false positive and false negative indications) made by dogs. The effects of the dogs’ routine e.g. omitting stations, using other cues than olfactory ones (visual) and effects of the non-verbal communications between handler and dog have been discussed.

A revision of the γ-evaluation concept for the comparison of dose distributions

A method for the quantitative four-dimensional (4D) evaluation of discrete dose data based on gradient-dependent local acceptance thresholds is presented. The method takes into account the local dose gradients of a reference distribution for critical appraisal of misalignment and collimation errors. These contribute to the maximum tolerable dose error at each evaluation point to which the local dose differences between comparison and reference data are compared. As shown, the presented concept is analogous to the γ-concept of Low et al (1998a Med. Phys. 25 656–61) if extended to (3+1) dimensions. The pointwise dose comparisons of the reformulated concept are easier to perform and speed up the evaluation process considerably, especially for fine-grid evaluations of 3D dose distributions. The occurrences of false negative indications due to the discrete nature of the data are reduced with the method. The presented method was applied to film-measured, clinical data and compared with γ-evaluations. 4D and 3D evaluations were performed. Comparisons prove that 4D evaluations have to be given priority, especially if complex treatment situations are verified, e.g., non-coplanar beam configurations.

Comparison of Serum β2-Microglobulin and Carcinoembryonic Antigen (CEA) in the Follow-Up of Breast Cancer Patients

Using commercially available radioimmune test kits, serial determinations of serum beta 2-microglobulin and CEA were performed in 337 patients, who had been treated for breast cancer by modified radical mastectomy and radiotherapy. The pre-therapeutic data indicated a higher incidence of pathological beta 2-microglobulin and CEA levels in patients with distant metastases than in patients with localized disease. However, this finding did not allow the conclusion of a direct complementarity of beta 2-microglobulin and CEA as tumour markers, since the group of patients with distant metastasis contained a high percentage of elderly patients who generally can be expected to have elevated beta 2-microglobulin serum concentrations. Therefore, the correlation of the clinical course of malignant disease and the incidence of relapses with the changes of serum beta 2-microglobulin and CEA concentrations was examined during the post-treatment surveillance: 7/9 cases (78%) with local recurrence and 46/73 cases (63%) with distant spread of disease were not indicated in the beta 2-microglobulin follow-up by pathologic serum concentrations, whereas in the CEA follow-up only 1/9 and 2/73 false negative indications were registered. The poor correlation suggests that serum beta 2-microglobulin is not directly tumour associated in breast cancer and does not fulfill the criteria of a tumour marker.

Is serum β2-microglobulin a tumor marker in gastrointestinal cancer?

Serial determinations of serum β2-microglobulin (β2m) and carcinoembryogenic antigen (CEA) were performed in 314 patients with histologically confirmed gastrointestinal cancer. The data were correlated with a set of clinical parameters. Pre-operative serum β2m levels did not discriminate different classes of tumor extension nor different stages of resectability of tumors in contrast to CEA. During post-operative surveillance the correlation of the time courses of serum β2m and CEA with the clinical course of malignant disease was studied in a selected group of 165 patients with resected primary carcinoma of the gastrointestinal tract. During the follow-up 74 165 patients showed disease progression or recurrence. In the β2m follow-up 66% false negative indications ( 49 74 ) of malignant disease were observed, whereas in the CEA follow-up it was 5% ( 4 74 ). The ratio of correct positive/false positive indications was 25 10 in the β2m follow-up and 70 10 in the CEA follow-up. The data indicate that the formation of serum β2m is not directly tumor associated in gastrointestinal cancer.