JPH0432737A - Abnormality detecting device for bearing - Google Patents
Abnormality detecting device for bearingInfo
- Publication number
- JPH0432737A JPH0432737A JP2139128A JP13912890A JPH0432737A JP H0432737 A JPH0432737 A JP H0432737A JP 2139128 A JP2139128 A JP 2139128A JP 13912890 A JP13912890 A JP 13912890A JP H0432737 A JPH0432737 A JP H0432737A
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- threshold value
- bearing
- signal
- period
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Links
- 230000005856 abnormality Effects 0.000 title claims abstract description 32
- 238000001514 detection method Methods 0.000 claims abstract description 20
- 238000005096 rolling process Methods 0.000 abstract description 6
- 238000000034 method Methods 0.000 description 14
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 description 6
- 230000002776 aggregation Effects 0.000 description 4
- 238000004220 aggregation Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
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- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
Description
この発明はアコースティックエミッノヨン(へト〕)を
利用した軸受の異常検出装置に関する。The present invention relates to a bearing abnormality detection device using an acoustic emitter.
従来、八Eによる軸受の異常検出装置としては次のよう
ならのかある(特開昭63−3(]4128)。この軸
受の異常検出装置は、AI・εセッサからのA、E信号
としきい値を比較する比較手段と、この比較手段からの
出力に基づいて、しきい値発超えたAE倍信号発生層I
I]1を算出する周1tl+算出手段と、この周期算出
手段の出力に基づいて、AC信号の発生周期毎の発生数
を集計する集計手段と、この集計手段で集計された発生
数が単一のしきい値を超えたか否かを判別する判別手段
とを備えて、AE倍信号発生周期毎の発生数を累積し、
その累積値がしきい値を超えた場合に、軸受の異常と判
断して、軸受以外のAEが発生ずる環境や外部ノイズが
大きい環境の下でも軸受の異常を検出できるようにして
いる。Conventionally, there is a bearing abnormality detection device based on 8E as shown below (Japanese Unexamined Patent Publication No. 63-3 (1983)). This bearing abnormality detection device uses the A and E signals from the AI/ε processor and Comparing means for comparing the values, and based on the output from this comparing means, the AE multiplied signal generation layer I exceeding the threshold value.
I] A frequency 1tl+ calculation means for calculating 1, a totaling means for totaling the number of occurrences for each generation cycle of AC signals based on the output of this period calculating means, and a totaling means for calculating the number of occurrences for each AC signal generation cycle, and a determining means for determining whether or not the threshold value of AE has been exceeded;
If the cumulative value exceeds a threshold value, it is determined that the bearing is abnormal, and the bearing abnormality can be detected even in an environment where AE other than the bearing occurs or where external noise is large.
しかしながら、」1記従来の軸受の異常検出装置では、
AE倍信号発生周期毎の発生数と単一のしきい値とを比
較して、異常の判別を行っているため、次の理由で検出
精度が低いという問題がある。
例えば、軸受が1回転する間に、内輪に異常がある場合
は、しきい値を超えるへE信号は1回しか発生しないが
、外輪に異常がある場合は、しきい値を超えるAE倍信
号約(転動体の個数/2)回だけ発生ずる。したがって
、内輪、外輪等の部位別にAE発生数と比較すべきしき
い値は異なるへきである。しかるに、に記従来の軸受の
異常検出装置は、総ての部位について、AE発生数と単
一のしきい値とを比較しているため、異常の検出精度が
低い。
そこで、この発明の目的は、部位別で異なる実際のAE
発生数を部位別の理論AE発生数で割ってAE発生確率
を求め、このAE発生確率と単一のしきい値とを比較す
ることによって、精度高く軸受の異常を検出できる軸受
の異常検出装置を提供することにある。However, in the conventional bearing abnormality detection device described in 1.
Since abnormalities are determined by comparing the number of occurrences per AE multiplied signal generation period with a single threshold value, there is a problem that detection accuracy is low for the following reason. For example, during one rotation of the bearing, if there is an abnormality in the inner ring, the E signal will be generated only once when the threshold is exceeded, but if there is an abnormality in the outer ring, the AE signal will be generated when the threshold is exceeded. This occurs approximately (number of rolling elements/2) times. Therefore, the threshold value to be compared with the number of AE occurrences is different for each part such as the inner ring and the outer ring. However, since the conventional bearing abnormality detection device described above compares the number of AE occurrences with a single threshold value for all parts, the accuracy of abnormality detection is low. Therefore, the purpose of this invention is to
A bearing abnormality detection device that can detect bearing abnormalities with high accuracy by dividing the number of occurrences by the theoretical number of AE occurrences for each part to find the probability of AE occurrence, and comparing this AE occurrence probability with a single threshold. Our goal is to provide the following.
上記目的を達成するため、この発明の軸受の異常検出装
置は、軸受からのアコースティックエミソソヨンを検出
してAE倍信号出力するAEセンサと、上記AEセセン
からのAE倍信号しきい値とを比較する比較手段と、」
1記比較手段から、」−記へE信号が上記しきい値を超
えたことを表わす信号を受けて、上記しきい値を超える
AE倍信号発生周期を算出する周期算出手段と、」1記
周期算出手段で算出された発生周期毎の発生数を集計す
る集計手段と、上記集計手段で集計された各発生周期毎
の発生数をその各発生周期に対応する部位別の理論発生
数で除算して、部位別のAE発生確率を算出するAE発
生確率算出手段と、上記AE発生確率算出手段から出力
されたAE発生確率がしきい値を超えたか否かを判別し
て軸受の異常を判別する判別手段とを備えたことを特徴
としている。In order to achieve the above object, the bearing abnormality detection device of the present invention includes an AE sensor that detects acoustic emission from the bearing and outputs an AE multiplied signal, and an AE multiplied signal threshold from the AE sensor. and a comparison means to compare.”
a period calculating means for receiving a signal indicating that the E signal exceeds the threshold value from the comparing means in item 1, and calculating an AE multiplied signal generation period exceeding the threshold value; a totalizing means for totalizing the number of occurrences for each occurrence period calculated by the period calculation means, and dividing the number of occurrences for each occurrence period calculated by the aggregation means by the theoretical number of occurrences for each site corresponding to each occurrence period; and an AE occurrence probability calculation means for calculating the AE occurrence probability for each part, and determining whether or not the AE occurrence probability outputted from the AE occurrence probability calculation means exceeds a threshold value to determine whether there is an abnormality in the bearing. The present invention is characterized in that it is equipped with a discriminating means.
軸受なとからのAEはAEセノザによって検出され、へ
I〕信号が出力される。このへE信号は比較手段てしき
い値と比較され、上記AE倍信号上記しきい値を超えた
ときにそれを表わす信号が出力される。この比較手段の
信号を受けて周期算出手段はAE倍信号周期を算出する
。上記集計手段は周期算出手段で算出された発生周期毎
のAE倍信号発生周期毎の発生数を集計する。上記AE
発生確率算出手段は、集計手段で集計された各発生周期
毎の発生数をその発生周期に対応する部位別の理論発生
数で除算して、部位別のAE発生確率を算出する。上記
判別手段は、AE発生確率算出手段から出力されたAE
発生確率と単一のしきい値と比較して、発生周期におけ
るAE発生確率がしきい値を超えたときに、軸受の異常
と判別する。
このように、AE発生確率をしきい値と比較するので、
部位毎にしきい値を異ならせる必要がなく、単一のしき
い値でもって精度よく軸受の診断をできる。
−に記AE発生確率がしきい値を超える発生周期によっ
て、内輪1転動体、外輪のいずれに損傷があるかが判断
できる。AE from the bearing is detected by the AE sensor, and a signal is output. This E signal is compared with a threshold value by a comparing means, and when the AE multiplied signal exceeds the threshold value, a signal representing this is output. In response to the signal from the comparing means, the period calculating means calculates the AE multiplied signal period. The aggregation means aggregates the number of occurrences of the AE multiplied signal for each generation cycle calculated by the cycle calculation means. The above AE
The occurrence probability calculation means divides the number of occurrences for each occurrence period, which is totaled by the aggregation means, by the theoretical number of occurrences for each region corresponding to the occurrence period, thereby calculating the probability of AE occurrence for each region. The above-mentioned discrimination means is an AE output from the AE occurrence probability calculation means.
The occurrence probability is compared with a single threshold value, and when the AE occurrence probability in the occurrence period exceeds the threshold value, it is determined that the bearing is abnormal. In this way, since the probability of AE occurrence is compared with the threshold,
There is no need to set different threshold values for each part, and bearings can be diagnosed with high accuracy using a single threshold value. - It can be determined whether the inner ring 1 rolling element or the outer ring is damaged, based on the occurrence cycle in which the AE occurrence probability exceeds the threshold value.
以下、この発明を図示の実施例により詳細に説明する。
第1図において、1は軸受などのAEを検出するΔr>
センサである。このAEセセン1から出力される八Eの
大きさを表わすAE倍信号プリアンプ2で増幅された後
、バンドパスフィルター3で例えば100KI−1zか
ら500KIIzの帯域のAE倍信号通過させられ、ノ
イズが除去される。上記バントパスフィルター3でノイ
ズが除去されたAE倍信号メインアンプ4でさらに増幅
され、包絡線検波回路5に入力され、包絡線検波される
。上記AE倍信号例えば第2図(a)のような波形を有
し、第2図(b)のようなスペクトルを示している。
包絡線検波回路5で包絡線検波された後の波形は第2図
(C)のようになる。包絡線検波された後のAE倍信号
比較器6においてしきい値と比較され、AE倍信号しき
い値を超えたときに第2図(d)に示すようなパルスを
コンピュータ7に出力する。
また、コンピュータ7には回転センサ8より単位時間あ
たりの軸受の回転数が入力される。
上記コンビコータ7においては第3図に示すようなフロ
ーヂャートによって処理が行なわれる。
まず、第3図のステップS1て初期設定がなされ、ステ
ップS2に進んで、予め定められた規定時間か経過した
か否かが判断される。ここで、規定時間が経過していな
いと判断された時はステップS3に進み、比較器6から
のパルスを受けたか否かによって、AEが発生したか否
かが判別され、AEが発生していないと判別したときは
ステップS2に戻り、AEが発生したと判別したときは
ステップS4に進み、このAEを検出した時刻A (1
)をメモリに記憶し、ステップS2に戻る。このAEの
発生した時刻A (1)は第5図に示している。」−記
ステップS2.S3.S4を繰り返した後、ステップS
2で規定時間が経過したと判断すると、ステップS5に
進んで、第4.5図に示すように、ステップS4で記憶
したAEの発生時刻A(+)Δ(2)、・・、A(4)
間の時間を表わす発生周期3口)、13(2)、・・、
13(6)が計算される。
この発生周期B(1)は第4図に示すザブルーヂンによ
って算出される。まず、ステップS21でI−1、N=
I 、に= Iとして初期設定がなされる。次いで、
ステップS22に進んで、発生周期B(1)がfA(2
)−A(1))として算出され、次いで、ステップS2
3に進み、Nが(発生数−1)になったか否かか判別さ
れる。いま、第5図に示すように、発生数が4で、I=
1だから、Nか3であるか否か判別される。いま、Nが
1であるから、ステップS24に進み、K=2.N=2
として、ステップS22に進んで、発生周期B(2)=
(Δ(3)−A(1))を求め、さらに、ステップS2
3.S24、S22を経て、発生周期B(3)=(A(
4)−A(I))を求める。次いで、ステップ823で
N=3となっているので、ステップS25に進んで、■
−(発生数−1)になったか否か、すなわちI=3で発
生周期B(6)を既に求めたか否かが判別される。いま
、I=1であるので、ステップ826に進んで、I=2
N=1として、ステップS22に戻り、発生周期B
(4)を求め、さらに、ステップS23.S2/I、S
22を経て発生周期B(5)を求ぬる。さらに、ステッ
プS23.S25,526S22を経て、発生周期B(
6)を求め、ステップS23.S25を経て、第7図で
示す発生周期の算出を終了する。そしてステップS6に
戻る。
ステップS6では、回転センサ8から受けた規定時間経
過中の軸受の単位時間当たりの回転数を基準回転数に換
算して、基準回転数にお(する周期にステップS5で算
出した周期を補正する。すなわち、ステップS5で算出
した周Mに対して、回転センサ8で検出した軸受の単位
時間当たりの回転数を掛け、基準回転数で割る処理を行
なう。次いで、ステップS7に進んで、各AE倍信号発
生周期毎に集計を行なう。すなわち、第2図(e)に示
すように、AEの発生周期毎の発生数を算出する。次い
で、ステップS8に進んで、周期別(部位別)のへE発
生数を周期別(部位別)の理論AE発生数で割って、へ
E発生確率を算出する。ここで、理論Δ■C発生数とは
、内輪、外輪、転動体の各部位について、1個所に損傷
が有るとして、軸受の1回転につき、各部位から発生ず
るAE発生数である。次いで、ステップS9に進んで、
第6図に示すように、各発生周期毎のA2発生確率が単
一のしきい値を超えたか否かを判別して、A2発生確率
り月二記しきい値を超えたときには軸受の損傷と判定し
、ステップSIOに進んで軸受の異常を表わす警報を出
力する。このように、部位別で異なるAE発生数を理論
AE発生数で除算して求めたA2発生確率としきい値と
を比較するので、各部位の損傷を単一のしきい値で精度
高く検出てきる。
上記AC発生確率がしきい値を超えた発生周期に基づい
て軸受の損傷箇所を識別することができる。ずなわぢ、
上記軸受が内輪を回転輪としたコロ軸受とすると内輪の
一定個所をコロが通過する周波数をFi、軸の回転周波
数をFr、外輪の所定個所を転動体が通過する周波数を
F。、コロの回転層波数をFb、保持器の回転周波数を
Pcとしたとき、
内輪に異常がある場合はI/Fi、l/Pr外輪に異常
がある場合はl /F’。
コロに異常がある場合はI/Fb、I/Fcの周期でも
ってAEが発生ずる。ずなイつち、内輪外輪、コロの各
損傷に応じてAEの発生周期が異なる。したかって、A
E発生確率がしきい値を超えた発生周期によって内輪と
外輪とコロのいずれに剥離が生じたかを判別することが
できる。
ステップS9で、どの発生周期においてもAE発生確率
がしきい値を超えないと判断したときはステップS2に
戻る。
この実施例は、I OOK、l−1z〜500KHzの
帯域を持ったバンドパスフィルタで雑音を除去した後、
一定以上の振幅を持ったAE倍信号検出し、このAE倍
信号周期毎の発生数を算出し、さらに、AE発生確率を
求め、このAE発生確率が単一のしきい値を超えた場合
に軸受の異常と判断するので、軸受に発生した剥離など
の異常を正確に検出でき、また、軸受の剥離部位を特定
できる。
また、軸受の回転数が変化した場合にAEの発生周期に
影響を及ぼすが、回転センサからの回転数を検出して発
生周期の補正を行なっているので、軸受の回転数の変動
の影響を受けずに、軸受の損傷個所を正確に特定するこ
とかできる。
また、特定の周期を持って発生ずるAE倍信号へE発生
確率でもって軸受の異常を判別するので、圧延機のメタ
ルイン時などのように軸受以外のAEが発生ずる環境で
、軸受に発生した剥離などの異常を正確に検出でき、ま
た、軸受の剥離部位を特定できる。
上記実施例では、AEの振幅と比較するしきい値は一定
値としたが、外部の雑音の大きさなどに応じてしきい値
を変動させてしよい。また、−1−記実施例では規定時
間終了後、AEの発生周期を補正しているが、AEの発
生の都度、AEの発生周期を補正してもよい。さらに、
回転数が一定の場合や回転数変化が予測される場合は、
回転センサ8を用いないで、コンピュータ7に回転数や
予測回転数を人力して、AEの発生周期を補正してしよ
い。回転数が一定であり、上記軸受の損傷部位の判定が
不要の場合はAEの発生周期の補正をしないことし可能
である。Hereinafter, the present invention will be explained in detail with reference to illustrated embodiments. In Fig. 1, 1 detects AE of bearings etc. Δr>
It is a sensor. After the AE multiplied signal representing the magnitude of 8E output from this AE sensor 1 is amplified by the preamplifier 2, the AE multiplied signal in the band from, for example, 100KI-1z to 500KIIz is passed through the bandpass filter 3, and noise is removed. be done. The AE multiplied signal from which noise has been removed by the band pass filter 3 is further amplified by the main amplifier 4, input to the envelope detection circuit 5, and subjected to envelope detection. The AE multiplied signal has a waveform as shown in FIG. 2(a), for example, and a spectrum as shown in FIG. 2(b). The waveform after envelope detection by the envelope detection circuit 5 is as shown in FIG. 2(C). After envelope detection, the AE multiplied signal is compared with a threshold in the AE multiplied signal comparator 6, and when the AE multiplied signal exceeds the threshold, a pulse as shown in FIG. 2(d) is outputted to the computer 7. Further, the number of rotations of the bearing per unit time is inputted to the computer 7 from the rotation sensor 8 . In the combination coater 7, processing is performed according to a flowchart as shown in FIG. First, initial settings are made in step S1 in FIG. 3, and the process proceeds to step S2, where it is determined whether a predetermined period of time has elapsed. Here, if it is determined that the specified time has not elapsed, the process proceeds to step S3, and it is determined whether or not AE has occurred depending on whether or not a pulse has been received from the comparator 6. If it is determined that no AE has occurred, the process returns to step S2, and if it is determined that an AE has occurred, the process proceeds to step S4, where the time A (1
) is stored in the memory, and the process returns to step S2. The time A (1) at which this AE occurred is shown in FIG. ”-Step S2. S3. After repeating S4, step S
If it is determined in step 2 that the specified time has elapsed, the process proceeds to step S5, and as shown in FIG. 4.5, the AE occurrence times A(+)Δ(2), . . . , A( 4)
Occurrence period 3 representing the time between), 13(2),...
13(6) is calculated. This generation cycle B(1) is calculated by the routine shown in FIG. First, in step S21, I-1, N=
I, is initialized as = I. Then,
Proceeding to step S22, the occurrence period B(1) is fA(2
)−A(1)), and then step S2
3, it is determined whether N has become (number of occurrences - 1). Now, as shown in Figure 5, the number of occurrences is 4 and I=
Since it is 1, it is determined whether it is N or 3. Now, since N is 1, the process advances to step S24 and K=2. N=2
, the process proceeds to step S22 and the occurrence period B(2)=
(Δ(3)-A(1)), and further step S2
3. After S24 and S22, the generation period B(3)=(A(
4) Find -A(I)). Next, since N=3 in step 823, the process proceeds to step S25, and
-(number of occurrences - 1), that is, whether or not the occurrence period B(6) has already been found at I=3. Since I=1 now, proceed to step 826 and set I=2.
With N=1, the process returns to step S22 and the occurrence period B
(4) is obtained, and further, step S23. S2/I,S
22, find the generation period B(5). Furthermore, step S23. After S25, 526 and S22, the generation cycle B (
6), and step S23. After S25, the calculation of the occurrence period shown in FIG. 7 is completed. Then, the process returns to step S6. In step S6, the number of revolutions per unit time of the bearing received from the rotation sensor 8 during a specified period of time is converted into a reference number of revolutions, and the period calculated in step S5 is corrected to the period to reach the reference number of revolutions. That is, the circumference M calculated in step S5 is multiplied by the number of revolutions per unit time of the bearing detected by the rotation sensor 8, and divided by the reference number of revolutions.Next, the process proceeds to step S7, and each AE Aggregation is performed for each double signal generation period. That is, as shown in FIG. Calculate the probability of E occurrence by dividing the number of E occurrences by the theoretical number of AE occurrences for each cycle (by location).Here, the theoretical number of Δ■C occurrences is for each location of the inner ring, outer ring, and rolling element. , is the number of AEs generated from each part per rotation of the bearing, assuming that there is damage in one part.Next, proceed to step S9,
As shown in Figure 6, it is determined whether the A2 occurrence probability exceeds a single threshold value for each occurrence cycle, and if the A2 occurrence probability exceeds the threshold value, bearing damage is determined. After making a determination, the process proceeds to step SIO and outputs an alarm indicating an abnormality in the bearing. In this way, the threshold value is compared with the A2 probability of occurrence, which is calculated by dividing the number of AE occurrences that vary by region by the theoretical number of AE occurrences, so damage to each region can be detected with high accuracy using a single threshold value. Ru. The damaged location of the bearing can be identified based on the occurrence cycle in which the AC occurrence probability exceeds the threshold value. Zunawaji,
If the above-mentioned bearing is a roller bearing with the inner ring as a rotating ring, Fi is the frequency at which the rollers pass through a certain part of the inner ring, Fr is the rotational frequency of the shaft, and F is the frequency at which the rolling elements pass at a certain part of the outer ring. , when the rotation layer wave number of the roller is Fb and the rotation frequency of the cage is Pc, if there is an abnormality in the inner ring, I/Fi, l/Pr if there is an abnormality in the outer ring, l/F'. If there is an abnormality in the roller, AE will occur at the I/Fb and I/Fc cycles. The cycle of AE occurrence differs depending on the damage to the inner ring, outer ring, and rollers. I want to, A.
It is possible to determine whether peeling has occurred in the inner ring, outer ring, or roller based on the occurrence cycle in which the E occurrence probability exceeds a threshold value. If it is determined in step S9 that the AE occurrence probability does not exceed the threshold in any occurrence period, the process returns to step S2. In this example, after removing noise with a bandpass filter having a band of IOOK, l-1z to 500KHz,
Detects an AE multiplied signal with an amplitude above a certain level, calculates the number of occurrences per period of this AE multiplied signal, and calculates the AE occurrence probability. Since it is determined that there is an abnormality in the bearing, it is possible to accurately detect abnormalities such as peeling that occur in the bearing, and also to identify the peeled part of the bearing. Additionally, changes in the bearing rotational speed will affect the AE occurrence cycle, but since the rotational speed from the rotation sensor is detected and the occurrence cycle is corrected, the effect of fluctuations in the bearing rotational speed can be reduced. It is possible to accurately identify the damaged location of the bearing without having to worry about damage. In addition, since bearing abnormalities are determined based on the E occurrence probability of the AE multiplied signal that occurs with a specific period, it is possible to detect abnormalities in bearings in environments where AE other than bearings occurs, such as during metal-in of a rolling mill. It is possible to accurately detect abnormalities such as peeling, and to identify the peeled part of the bearing. In the embodiments described above, the threshold value for comparison with the AE amplitude is set to a constant value, but the threshold value may be varied depending on the magnitude of external noise or the like. Furthermore, in the embodiment described in -1- above, the AE generation cycle is corrected after the specified time has elapsed, but the AE generation cycle may be corrected each time an AE occurs. moreover,
If the rotation speed is constant or a change in rotation speed is expected,
Instead of using the rotation sensor 8, the rotation speed and predicted rotation speed may be manually entered into the computer 7 to correct the AE occurrence cycle. If the rotation speed is constant and it is not necessary to determine the damaged portion of the bearing, it is possible to not correct the AE occurrence cycle.
以上より明らかなように、この発明のM受の異常検出装
置は、AEセンサからのAE倍信号しきい値を比較する
比較手段と、しきい値を超えたAE倍信号発生周期を算
出する周期算出手段と、AE倍信号発生周期毎の発生数
を集計する集計手段と、集計手段で集計された発生部位
tjj t)) A E発生数を理論AE発生数で除算
するAE発生確率算出手段と、AE発生確率がしきい値
を超えたか否かを判別する判別手段を備えて、発生周期
毎のAE発生確率と単一のしきい値とを比較して、軸受
の異常を判別するので、軸受の異常を単一のしきい値で
もって精度よく、かつ発生個所を特定して検出すること
ができる。As is clear from the above, the M receiver abnormality detection device of the present invention includes a comparison means for comparing threshold values of the AE multiplied signal from the AE sensor, and a period for calculating the generation period of the AE multiplied signal that exceeds the threshold. a calculation means, a totalization means for totalizing the number of occurrences for each AE multiplication signal generation cycle, and an AE occurrence probability calculation means for dividing the number of occurrences of AE by the theoretical number of AE occurrences. , a determination means for determining whether or not the AE occurrence probability exceeds a threshold value is provided, and an abnormality in the bearing is determined by comparing the AE occurrence probability for each occurrence cycle with a single threshold value. Bearing abnormalities can be detected with high precision and the location where they occur using a single threshold value.
第1図はこの発明の軸受の異常検出装置のブロック図、
第2図(a)、(b)、(c)、(d)、(e)はこの
実施例の各部分における波形を示す図、第3図は上記実
施例のフ〔J−チャート、第4図はAE発生周期を算出
するザブルーチンを示すフローチャート、第5図は発生
時刻と発生周期との関係を示す図、第6図は発生周期と
AE発生確率としきい値との関係を示す図である。
ATεセンセン 3 バントパスフィルター5・・包
絡線検波回路、6・・比較器、7・・コンビコータ、
8・・・回転センサ。
特 許 出 願 人 光汀精工株式会社 外1名代理
人 弁理士 青 山 葆 外1名第2図
(a)
(b)
忙)
+d)
(e)
第3図FIG. 1 is a block diagram of the bearing abnormality detection device of the present invention.
FIGS. 2(a), (b), (c), (d), and (e) are diagrams showing waveforms in each part of this embodiment, and FIG. 3 is a graph of the above embodiment [J-chart, Figure 4 is a flowchart showing the subroutine for calculating the AE occurrence cycle, Figure 5 is a diagram showing the relationship between the occurrence time and the occurrence cycle, and Figure 6 is a diagram showing the relationship between the occurrence cycle, the AE occurrence probability, and the threshold value. be. ATε sensor 3 Band pass filter 5... Envelope detection circuit, 6... Comparator, 7... Combi coater,
8... Rotation sensor. Patent applicant: Koten Seiko Co., Ltd. and one other representative
Person Patent attorney Aoyama Ao and one other person Figure 2 (a) (b) Busy) +d) (e) Figure 3
Claims (1)
してAE信号を出力するAEセンサと、上記AEセンサ
からのAE信号としきい値とを比較する比較手段と、 上記比較手段から、上記AE信号が上記しきい値を超え
たことを表わす信号を受けて、上記しきい値を超えるA
E信号の発生周期を算出する周期算出手段と、 上記周期算出手段で算出された発生周期毎の発生数を集
計する集計手段と、 上記集計手段で集計された各発生周期毎の発生数をその
各発生周期に対応する部位別の理論発生数で除算して、
部位別のAE発生確率を算出するAE発生確率算出手段
と、 上記AE発生確率算出手段から出力されたAE発生確率
がしきい値を超えたか否かを判別して軸受の異常を判別
する判別手段とを備えたことを特徴とする軸受の異常検
出装置。(1) an AE sensor that detects acoustic emissions from a bearing and outputs an AE signal; a comparison means that compares the AE signal from the AE sensor with a threshold value; A that exceeds the above threshold upon receiving a signal indicating that the threshold has been exceeded.
a period calculation means for calculating the generation period of the E signal; a totaling means for totaling the number of occurrences for each generation period calculated by the period calculation means; Divide by the theoretical number of occurrences for each site corresponding to each generation cycle,
AE occurrence probability calculation means for calculating the AE occurrence probability for each part; and determination means for determining whether or not the AE occurrence probability outputted from the AE occurrence probability calculation means exceeds a threshold value to determine whether there is an abnormality in the bearing. A bearing abnormality detection device characterized by comprising:
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2139128A JP2963144B2 (en) | 1990-05-29 | 1990-05-29 | Bearing abnormality detection device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2139128A JP2963144B2 (en) | 1990-05-29 | 1990-05-29 | Bearing abnormality detection device |
Publications (2)
Publication Number | Publication Date |
---|---|
JPH0432737A true JPH0432737A (en) | 1992-02-04 |
JP2963144B2 JP2963144B2 (en) | 1999-10-12 |
Family
ID=15238175
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP2139128A Expired - Fee Related JP2963144B2 (en) | 1990-05-29 | 1990-05-29 | Bearing abnormality detection device |
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Country | Link |
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JP (1) | JP2963144B2 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0982579A1 (en) * | 1998-03-16 | 2000-03-01 | Central Japan Railway Company | Devices for inspecting bearings of main motors of rolling stock |
CN102830177A (en) * | 2011-06-13 | 2012-12-19 | 松下电器产业株式会社 | Abnormality checking apparatus and abnormality checking method |
CN108956145A (en) * | 2018-07-17 | 2018-12-07 | 中国科学院沈阳自动化研究所 | Based on the lossless sparse Fault Diagnosis of Roller Bearings from coding of constraint noise reduction |
CN114025938A (en) * | 2019-07-02 | 2022-02-08 | 芝浦机械株式会社 | Abnormality detection device for extrusion molding machine |
-
1990
- 1990-05-29 JP JP2139128A patent/JP2963144B2/en not_active Expired - Fee Related
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0982579A1 (en) * | 1998-03-16 | 2000-03-01 | Central Japan Railway Company | Devices for inspecting bearings of main motors of rolling stock |
EP0982579A4 (en) * | 1998-03-16 | 2006-03-22 | Tokai Ryokaku Tetsudo Kk | Devices for inspecting bearings of main motors of rolling stock |
CN102830177A (en) * | 2011-06-13 | 2012-12-19 | 松下电器产业株式会社 | Abnormality checking apparatus and abnormality checking method |
CN102830177B (en) * | 2011-06-13 | 2014-11-12 | 松下电器产业株式会社 | Abnormality checking method and abnormality checking apparatus |
CN108956145A (en) * | 2018-07-17 | 2018-12-07 | 中国科学院沈阳自动化研究所 | Based on the lossless sparse Fault Diagnosis of Roller Bearings from coding of constraint noise reduction |
CN114025938A (en) * | 2019-07-02 | 2022-02-08 | 芝浦机械株式会社 | Abnormality detection device for extrusion molding machine |
CN114025938B (en) * | 2019-07-02 | 2023-09-19 | 芝浦机械株式会社 | Abnormality detection device for extrusion molding machine |
Also Published As
Publication number | Publication date |
---|---|
JP2963144B2 (en) | 1999-10-12 |
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