NOx Sensor Research Articles

Performance improvement of NiO/YSZ sensitive electrode for high temperature electrochemical NOx gas sensors

In the mixed-potential gas sensor, the electrode type, particle size, microstructural morphology, as well as the interface state between the oxide-sensitive electrode and the solid electrolyte, are the key factors for determining the magnitude of the mixed potential. NiO-based material with the addition of Y2O3-stabilized ZrO2 (YSZ) particles is considered as one of the promising sensitive electrode materials for preparing the mixed-potential NOx sensors due to its excellent gas sensitivity. In this paper, the preparation technology of NiO-sensitive electrodes is developed and optimized for improving NOx (NO and NO2) gas-sensitivity properties of the NOx sensors. The experimental results show that the addition of YSZ can effectively improve the agglomeration morphology of NiO particles in the NOx-YSZ-based electrode material during high temperature sintering (i.e. 1200 °C) and enhance the bonding strength with the solid electrolyte. The sensor which was prepared by using the developed NOx-YSZ-based electrode material with the YSZ addition of 20 wt% has the most sensitive response characteristic to NO gas, and its response potential value increases with NO gas concentration. Elevating electrode sintering temperature can increase the size of NiO particles and shorten the length of the three-phase boundary (TPB) in the sensitive electrode. The average length of TPB is the longest in the sensitive electrode sintered at 1000 °C which corresponds to the best sensitive response to NO gas. The sensitive electrode's thickness also affects the NO response sensitivity, and the sensor with an electrode thickness of 14 μm has the most NO response sensitivity. In addition, the NO/NO2 gas mixture test results indicate that the current developed sensor is more sensitive to NO2 gas than that of NO gas.

Harnessing NOx emission management: A virtual sensor model for natural gas power generation engines with active pre-chamber

This paper presents a NOx Sensor machine learning model for a highly efficient 2MW power generation lean burn gas engine with an active pre-chamber. As gas power generation continues to evolve, engine efficiency and the accurate measurement of NOx emissions has taken on greater importance. However, the working life of existing NOx sensors pales in comparison to the lifecycle of an engine. This paper studies a series of machine learning NOx sensor models and carries out a comparative analysis. We introduce a predictive analytics model used for monitoring the emissions of active pre-chamber gas engines. This real deployment aims to detect the deviations in emissions across an entire fleet of gas engines, even when no physical sensors are installed in the engines. Finally, a model has been implemented that allows the prediction of NOx values, in engines that did not have them initially, with a 5–10 % precision. For example, with NOx at 120 ppm, the virtual sensor provides values close to +-10 ppm. It has been observed that such features as the setpoint value of the active pre-chamber gas pressure, in a lean burn gas engine with active pre-chamber, are among those parameters that have the greatest impact on the model estimation.

Mechanistic insight and first principle analysis of cation-inverted zinc ferrite nanostructure: A paradigm for ppb-level room temperature NOx sensor

Herein, we adopted a new paradigm for developing a high-performance gas sensor by leveraging the mixed spinel ZnFe2O4 structure (mZFO) to enhance the adsorption of NOx molecules. Material characterization reveals the formation of the mZFO due to the cation inversion in lattice sites. The estimated value of the inversion degree is observed to shift from 0.78 to 0.39 with an increase in the calcination temperature. The mZFO nanoparticles calcined at 500 °C show exceptional sensing performance due to their suitable grain size (∼2 times Debye length), neck diameter, and surface area. The sensing studies conducted at various NOx concentrations indicate that the sensor can detect ppb level of NOx with a detection limit of about 9 ppb at room temperature. The detailed sensing mechanism is elucidated based on the density functional theory calculations (DFT) and Bader charge analysis. The outstanding sensor performance is attributed to the formation of a mixed spinel structure, wherein the adsorption energy of NOx (∼-0.6 eV) in the presence of surface adsorbed oxygen is higher than that of the normal spinel structure (∼-0.1 eV). Furthermore, the sensor exhibited a fast response and recovery times (7 and 92 s at 800 ppb NO2), excellent stability, and selectivity. The practical suitability of the mZFO sensor was studied by analyzing the vehicle exhaust emissions. We strongly believe this work would pave a novel approach to developing a high-potential gas sensor by modifying the cation distributions in the spinel ferrites.

Controllable sintering behaviors of Al2O3 green tapes and co-firing compatibility of Al2O3/YSZ ceramics

As a typical insulating material, Al2O3 serves as the signal isolation device in multi-layer ceramic NOx sensors. However, the application of Al2O3-based insulating materials is limited by the co-firing compatibility between Al2O3 and yttria-stabilized zirconia (YSZ). In the present study, Al2O3 green tapes with different contents of γ-Al2O3 and SiO2–B2O3 (Si–B) are developed to promote the integration of Al2O3-based insulating material and YSZ. The sintering shrinkage of the obtained Al2O3 green tapes is greatly influenced by the ratio of γ-Al2O3 to α-Al2O3 at 1450 °C, e.g., the maximum shrinkage reaches 17.34% when the γ-Al2O3 to α-Al2O3 ratio is 1:2. The addition of Si–B greatly increases the low density of Al2O3 tapes due to the pores generated during the firing process of the ultrafine γ-Al2O3 powders. The sintering density increases to 97.1% when the Si–B content is 15 wt. %, and the sintering shrinkage reaches a relatively higher value of 17.25%. The obtained Al2O3-based insulating materials show a greatly co-firing matching performance with the YSZ green tapes. This work will contribute to further investigation of ceramic co-firing studies.

Adaptive time-step unscented kalman filtering (ATS-UKF) based observer design for urea selective catalytic reduction (SCR) performance of diesel engines

To reduce the number of sensors in the SCR catalyst, state feedback and fault diagnosis information are provided. Firstly, a model based on the coupling of flow, heat transfer, and gas-solid phase catalytic reaction in the SCR system is investigated in this paper. The parabolic partial differential equations are simplified by the variable substitution method and the method of lines approach (MOL). The simplified system of equations is solved by backward differentiation formulas (BDF) with adaptive adjustment time step strategy. Meanwhile, the chemical reaction parameters are accurately calibrated per second using the Levenberg-Marquardt method. Secondly, the ATS-UKF is designed in this paper, and to ensure the synchronisation between the ATS-UKF and the SCR model calculations, the time step of solving the BDF by the SCR model is taken as the time step of propagating the sigma points. Two observation scenarios are assumed: (1) no downstream NH3 concentration sensor, ammonia coverage and downstream NH3 concentration are observed by ATS-UKF; (2) no downstream NOx sensor, ammonia coverage and downstream NOx concentration are observed by ATS-UKF. Finally, the paper carries out bench tests. In the first case, the ammonia coverage obtained by the ATS-UKF reached 0.99 with respect to the model-calculated value R². The mean absolute error (MAE) between the observed and experimental values of the ATS-UKF for the downstream NH3 concentration was 2.76 ppm. In the second case, the ammonia coverage obtained by the ATS-UKF reached 0.99 with respect to the model-calculated value R², and the MAE between the observed and experimental values of the ATS-UKF for the downstream NOx concentration was 1.53 ppm. Environmental ImplicationThe Adaptive Time-Step Unscented Kalman Filtering (ATS-UKF) enhances urea Selective Catalytic Reduction (SCR) in diesel engines, improving environmental outcomes. This method minimizes sensor dependence, enabling more precise SCR system management and effective emission reduction. By advancing emission control technologies, ATS-UKF contributes to global air pollution mitigation efforts, supporting cleaner air and environmental sustainability. Its innovative approach in monitoring and predicting SCR performance marks a significant step towards eco-friendly diesel engine operation.

Data-driven machine learning model of a Selective Catalytic Reduction on Filter (SCRF) in a heavy-duty diesel engine: A comparison of Artificial Neural Network with Tree-based algorithms

The Selective Catalytic Reduction on Filter (SCRF) system is yet to be deployed in current heavy-duty diesel engine aftertreatment system. Due to the thermal, space and cost benefits of the SCRF, it could become a useful component of the after-treatment system of a heavy-duty diesel engine, as regulators continue to demand an even cleaner environment. Ammonia cross-sensitivity of NOx sensors at the post-SCRF location poses challenges in measuring NOx emission accurately at this location and in turn affects the NOx conversion efficiency calculations. Developing a model that could replace the NOx sensor helps to mitigate the ammonia cross-sensitivity challenge as well as provides a medium to measure post-SCRF NOx concentration and NOx conversion efficiency while saving the cost on NOx sensors. This work focuses on a data-driven approach to developing a model for predicting NOx conversion efficiency across the SCRF using Artificial Neural Network, Bootstrap Forest, and Boosted Tree methods. Further, the three modeling techniques were also compared for accuracy and computation cost.

A Fuzzy Decoupling Compensator With Direction Control for NOx Sensor

A Fuzzy Decoupling Compensator With Direction Control for NOx Sensor

Protection of NOx Sensors from Sulfur Poisoning in Glass Furnaces by the Optimization of a "SO2 Trap".

Electrochemical NOx sensors based on yttria-stabilized zirconia (YSZ) provide a reliable onboard way to control NOx emissions from glass-melting furnaces. The main limitation is the poisoning of this sensor by sulfur oxides (SOx) contained in the stream. To overcome this drawback, an "SO2 trap" with high SOx storage capacity and low affinity to NOx is required. Two CuO/BaO/SBA-15 traps with the same CuO loading (6.5 wt.%) and different BaO loadings (5 and 24.5 wt.%, respectively) were synthetized, thoroughly characterized and evaluated as SO2 traps. The results show that the 6.5%CuO/5%BaO/SBA-15 trap displays the highest SO2 adsorption capacity and can fully adsorb SO2 for a specific period of time, while additionally displaying a very low NO adsorption capacity. A suitable quantity of this material located upstream of the sensor could provide total protection of the NOx sensor against sulfur poisoning in glass-furnace exhausts.

Assessment of Heavy-Duty Diesel Vehicle NOx and CO2 Emissions Based on OBD Data

Controlling NOx and CO2 emissions from heavy-duty diesel vehicles (HDDVs) is receiving increasing attention. Accurate measurement of HDDV NOx and CO2 emissions is the prerequisite for HDDV emission control. Vehicle emission regulations srecommend the measurement of NOx and CO2 emissions from vehicles using an emission analyzer, which is expensive and unsuitable to measure a large number of vehicles in a short time. The on-board diagnostics (OBD) data stream of HDDVs provides great convenience for calculating vehicle NOx and CO2 emissions by providing the engine fuel flow rate, NOx sensor output, and air mass flow. The calculated vehicle NOx and CO2 emissions based on the OBD data were validated by testing a heavy-duty truck’s emissions on the chassis dynamometer over the CHTC-HT driving cycle, showing that the calculated NOx and CO2 emissions based on the OBD data are consistent with the measured results by the emission analyzer. The calculated vehicle fuel consumptions based on the OBD data were close to the calculated results based on the carbon balance method and the measured results by the fuel flowmeter. The experimental results show that accessing vehicle NOx and CO2 emissions based on the OBD data is a convenient and applicable method.

NOx Sensor Constructed from Conductive Metal-Organic Framework and Graphene for Airway Inflammation Screening.

The detection of nitric oxide in human exhaled breath (EB) has received wide attention due to its close relationship with respiratory tract inflammation. Herein, a ppb-level NOx chemiresistive sensor was prepared by assembling graphene oxide (GO) with a conductive π-d conjugated metal-organic framework Co3(HITP)2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene) in the presence of poly(dimethyldiallylammonium chloride) (PDDA). The construction of a gas sensor chip was achieved by drop-casting the GO/PDDA/Co3(HITP)2 composite onto ITO-PET interdigital electrodes, followed by in situ reduction of GO to reduced graphene oxide (rGO) in hydrazine hydrate vapor. Compared with bare rGO, the nanocomposite shows significantly improved sensitivity and selectivity for NOx among various gas analytes owing to its folded and porous structure as well as its numerous active sites. The limit of detection (LOD) for NO and NO2 can reach as low as 11.2 and 6.8 ppb, respectively, and the response/recovery time to 200 ppb NO is 24/41 s. These results indicate that rGO/PDDA/Co3(HITP)2 can achieve a sensitive and fast response toward NOx at room temperature (RT). Additionally, good repeatability and long-term stability were observed. Furthermore, the sensor shows improved humidity tolerance owing to the presence of hydrophobic benzene rings in Co3(HITP)2. To demonstrate its ability in EB detection, EB samples collected from healthy individuals were spiked with a certain amount of NO to simulate the EB of respiratory inflammatory patients. The sensor can successfully distinguish healthy people from the simulated patients. Furthermore, in real clinical sample detection, the sensor can further differentiate acute respiratory inflammatory patients from the chronic ones.

Development of a NOx Calculation Model for Low-Speed Marine Diesel Engines Based on Soft Measurement Technology

With the increasing level of intelligence of marine engines, there is an increasing demand for the online monitoring of engines, and marine NOx emissions have been of great concern. In this paper, a NOx simulation model is developed based on virtual measurement technology, which can calculate and predict NOx emissions based on the current operating state parameters of low-speed two-stroke diesel engines. First, the calibrated 3D simulation model is used to design the experiments to obtain the simulation experimental samples. Based on the NOx generation mechanism and diesel engine work-related parameters, the relevant factors were selected as alternative input parameters for the NOx emission model. The correlation analysis was then performed on the input parameters using the grey relational analysis correlation method and the Pearson correlation coefficient, and the principal component analysis method was used to reduce the dimensionality of the relevant factors by minimizing the loss of important information in reducing the complexity of the whole model. Then, the structure-related parameters of the backpropagation neural network (BPNN) were adaptively optimized using the group method of data handling (GMDH) to improve the accuracy of the NOx soft measurement model. Finally, the developed GMDH–BP model was validated with data and compared with the error evaluation index of BPNN and BPNN optimized by genetic algorithm (GA), and the developed NOx simulation model demonstrated high prediction accuracy under the same hyperparameter settings. The result provides technical support for the subsequent realization of the real-time online monitoring of NOx emissions from low-speed marine diesel engines without NOx sensors.

Enhancement of La0.8Sr0.2MnO3-based amperometric total NOX sensor via modification with Pt nanoparticles

Pt nanoparticle (PtNP)-modified La0.8Sr0.2MnO3 (LSM)-sensing electrodes (SE) were developed successfully by the impregnation method for amperometric total NOX (NO and NO2) sensors. The structure, morphology, and surface valence state of SE were characterized by thermogravimetry (TG), X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The results showed that all SEs of the sensors were uniformly modified with PtNPs; interestingly, the size of the PtNPs increased with an increase in the modified content. Compared with pure LSM, the sensor based on 0.50 vol% Pt/LSM exhibited enhanced gas-sensing properties toward total NOX. The sensitivity improved from − 9.04 to − 18.7 μA/decade in the NOX, ranging from 50 ppm to 800 ppm. Moreover, the sensor maintained a 94% response signal after a long-term stability test, showing a stable response during the long-term thermal cycling test towards total NOX. Meanwhile, the sensor exhibited markable selectivity and consistency. The catalytic effect of Pt nanoparticles was believed to be the main reason for the enhancement of gas-sensing characteristics.

Conductometric room temperature NOx sensor based on metal-organic framework-derived Fe2O3/Co3O4 nanocomposite

In this work, Fe2O3/Co3O4 heterostructures were prepared via the ion-exchange between Co-MOFs template and Fe3+. At room temperature (25 °C), gas-sensing measurements revealed the Fe2O3/Co3O4 composites showed high response (Ra/Rg =2.54, 97.0 ppm), excellent selectivity and low detection limit (0.97 ppm) to NOx. The special oxygen temperature-programmed desorption (O2-TPD) and hydrogen temperature-programmed reduction (H2-TPR) curves indicated addition of Fe not only damaged the structure, but also increased the active sites of composites. Mott-Schottky (MS) plots testing and Electrochemical Impedance Spectroscopy (EIS) analysis were used to further quantitatively analyze the effect of Fe content on carrier density (Nd) and charge transfer resistance (Rct). The enhanced sensing mechanism of Fe2O3/Co3O4 composites were attributed to the as-formed heterojunctions that offered numerous diffusion channels and the additional modulation of resistance. By employing a facile MOFs hybrid-assisted method to design heterojunction composites, this work comprehensively analyzed the influence of heterogeneous structure on gas sensitivity, providing insights into the understanding of heterojunction gas sensor materials.

Dual mid-infrared wavelength Faraday rotation spectroscopy NOx sensor based on NdFeB ring magnet array

Faraday rotation spectroscopy (FRS) exploits the magneto-optical Faraday rotation effect to achieve highly sensitive detection of paramagnetic molecules. Most of the currently reported FRS sensors suffer from high power consumption, while enabling detection of only a single component. In this work, a novel dual mid-infrared wavelength FRS sensor is proposed that enables the simultaneous detection of two paramagnetic molecules (nitrogen oxides, NOx = NO + NO2) in a single absorption cell. A neodymium-iron-boron (NdFeB) ring magnet array was designed to replace the solenoid coil to provide a stable, power-free magnetic field. The magnetic induction line distribution characteristics around the permanent magnets were analyzed based on the finite element method. Two mid-infrared quantum cascade lasers were used to detect the strongest fundamental ν2 rotational-vibrational band of NO (1875.81 cm−1) and the ν3 fundamental band of NO2 (1613.25 cm−1), respectively. A dual-wavelength Herriott cell (DWHC) is coaxially combined with a NdFeB ring magnet array to amplify the interaction of the laser beam with paramagnetic NOx molecules in an axial magnetic field. This low-power FRS NOx sensor achieves minimum detection limits of 0.58 ppb and 0.95 ppb for NO2 and NO, respectively, over an optical length of 23.7 m.

Sintering characteristics and electrical properties of 5YSZ electrolyte

5YSZ (5 mol. %Y2O3 stabilized ZrO2) solid electrolytes play a key role in NOx sensors for their important ion conduction performance in the sensing process. In this study, the influence of sintering and test temperature on 5YSZ solid electrolytes was investigated. The 5YSZ solid electrolyte was prepared by the tape casting process, followed by the sintering process. The ionic conductivity of the 5YSZ solid electrolyte sintered at 1450 °C is higher than that of other samples. The conductivity and activation energy of grain interior and grain boundary showed opposite tendency. The performance of ion conductivity was attributed to (1) the higher test temperature being applied to the working electrolyte and accelerated transfer efficiency and (2) enhanced diffusion of oxygen ions, which increased through the formation of dense structure.

Diagnostics-oriented Model for Automotive SCR-ASC

This paper presents a diagnostics-oriented aging model for combined Selective Catalytic Reduction (SCR) and Ammonia Slip Catalyst (ASC) system, along with a model-based on-board diagnostic (OBD) method applied to both test-cell data and on-road data from commercial trucks. The key challenge with model development was unavailability of NOx and NH3 measurements between SCR and ASC. Since it would have been very difficult to calibrate both SCR and ASC dynamics without any measurements between SCR and ASC, therefore ASC was modeled using static look-up tables to determine ASC’s NH3 conversion efficiency and its selectivity to NOx and N2O as a function of temperature and flow rate. The traditional three-state single-cell ordinary differential equation (ODE) model was used for SCR. Hot Federal Test Procedure (hFTP) was used to calibrate the model. Cold FTP (cFTP) and Ramped Mode Cycle (RMC) were used for validation. Results show that the SCR-ASC model can capture the aging signatures in tailpipe NOx, NH3, and N2O reasonably well for cFTP, hFTP, and RMC cycles in the testcell data. After slight re-calibration and combining with a simple model for commercial NOx sensor’s cross-sensitivity to NH3, the model works reasonably well for on-road data from commercial trucks. A model-based on-board diagnostic (OBD) method has been presented with enable conditions designed to detect operating conditions suitable for detecting aging signatures, while minimizing false positives and false negatives. The OBD method is applied to both test-cell and real-world truck data with commercial NOx sensors. Results on test-cell data demonstrate the challenges of robust SCR monitoring even on the limited data set used in this work. The model-based enable conditions are shown to be robust but extremely restrictive as the OBD gets enabled at very few points in the test-cell data. Application on truck data showed that the proposed OBD method can be implemented on commercial trucks with limited sensors. In the truck data, the enable conditions were satisfied on many more points than the test-cell data. Results on truck data show encouraging trends between relative degradation level and the number of miles on four trucks. In future work, these trends will be validated using more data from commercial trucks with known aging levels.

Highly Selective Room Temperature Detection of NH3 and NOx Using Oxygen-Deficient W18O49-Supported WS2 Heterojunctions

In this paper, we reported the controlled synthesis of tungsten disulfide/reduced tungsten oxide (WS2/W18O49) heterojunctions for highly efficient room temperature NOx and ammonia (NH3) sensors. X-ray diffraction analysis revealed the formation of the oxygen-deficient W18O49 phase along with WS2. Field-emission scanning electron microscopy and transmission electron microscopy displayed the formation of WS2 flakes over W18O49 nanorods. X-ray photoelectron spectroscopy showed the presence of tungsten in W4+, W5+, and W6+ oxidation states corresponding to WS2 and W18O49, respectively. The WS2/W18O49 heterojunction sensor exhibited sub-ppm level sensitivity to NOx and NH3 at room temperature. The heterojunction sensor detected 0.6 ppm NOx and 0.5 ppm NH3, with a corresponding response of 7.1 and 3.8%, respectively. The limit of detection of the sensor was calculated to be 0.05 and 0.17 ppm for NH3 and NOx, respectively. The cyclic stability test showed that the sensor exhibited high stability even after 24 cycles for the detection of NH3 and 14 cycles for NOx. Compared to pristine WO3 and WS2, the WS2/W18O49 heterojunction showed high selectivity toward NOx and NH3. The results could be useful for the development of room temperature NOx and NH3 sensors.

Synergistic Au passivation and prolonged aging optimization enhance the long-term catalytic stability of porous YSZ/Pt electrodes

The catalytic activity of an oxygen-pumping electrode is very important to the performance of amperometric NOx (NO and NO2) sensors. To enhance the stability of electrochemical NOx sensors based on Pt electrodes, we fabricated sensitive electrodes of Pt-based materials doped with small and different Au contents on 8% mol Y2O3-stabilized ZrO2 (8YSZ) solid electrolyte substrates by screen printing and high-temperature co-firing. The effects of Au on the porous Pt electrode’s catalytic activity and long-term stability were investigated, and the results are as follows. First, the catalytic capacity of the Pt–Au alloy electrode decreases with increasing Au content due to the inhibition of the oxygen adsorption on Au, which decreases the amount of oxygen participating in the reaction at triple-phase boundaries (TPBs). Second, the catalytic ability of the alloy electrode decreases exponentially with aging time due to the reduction in the length of TPBs caused by the significant reduction in the number of holes in the electrode, and the oxidation of Pt. In addition, the conductivity decay rate of the electrode decreases with increasing Au doping. This indicates that Pt-based electrodes doped with varying Au contents, long-term, and high-temperature aging have notably regular effects on the catalytic activity of oxygen-pumping electrodes, for which we have developed a model of Au passivation and the synergistic effect of aging time on the activity of Pt-based oxygen-pumping electrodes.

Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method

A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.

Research on fault diagnosis and signal reconstruction technology of diesel engine NOx sensor based on deep learning algorithm

Modern diesel engines are commonly equipped with NOx sensors for precise and efficient selective catalytic reduction (SCR) reductant injection control, but due to the limitations of operating characteristics and environmental conditions, the output signal of NOx sensors may show abnormalities such as drift, deviation, or even distortion. Therefore, the evaluation and adjustment of the NOx sensor signal is essential to achieve intelligent control of the engine SCR system. In this study, an engine NOx calculation model is designed using deep neural network (DNN) to predict the upstream NOx emission concentration of engine SCR and provide reference values for sensor fault diagnosis, evaluate the sensor fault status by calculating the relative integration error index (IE), and reconstruct the sensor signal using the model prediction signal after the fault is confirmed. In addition, this paper uses the firefly algorithm (FA) to find the appropriate diagnostic threshold (IEthre) and integration period ( T) to meet the requirements of diagnosis response speed. Finally, based on the data collected from World Harmonized Transient Cycle (WHTC) bench test of a commercial high-pressure common rail diesel engine, the IEthre = 12.76% and the T = 7.57 s. The root mean square error (RSME) of the NOx concentration signal predicted by the engine NOx calculation model was 37.35 ppm, and the normalized cumulative NOx emission was 98% of the actual signal. The performance of the designed algorithm was tested using the synthesized bias fault and drift fault signals. The sensor signal RSME was reduced from 108.46 to 29.66 ppm for the bias fault, and from 106.92 to 31.30 ppm for the drift fault.